@article {pmid35594931,
year = {2022},
author = {Mashrur, FR and Rahman, KM and Miya, MTI and Vaidyanathan, R and Anwar, SF and Sarker, F and Mamun, KA},
title = {An Intelligent Neuromarketing System for Predicting Consumers' Future Choice from Electroencephalography Signals.},
journal = {Physiology & behavior},
volume = {},
number = {},
pages = {113847},
doi = {10.1016/j.physbeh.2022.113847},
pmid = {35594931},
issn = {1873-507X},
abstract = {Neuromarketing utilizes Brain-Computer Interface (BCI) technologies to provide insight into consumers responses on marketing stimuli. In order to achieve insight information, marketers spend about $400 billion annually on marketing, promotion, and advertisement using traditional marketing research tools. In addition, these tools like personal depth interviews, surveys, focus group discussions, etc. are expensive and frequently criticized for failing to extract actual consumer preferences. Neuromarketing, on the other hand, promises to overcome such constraints. In this work, an EEG-based neuromarketing framework is employed for predicting consumer future choice (affective attitude) while they view E-commerce products. After preprocessing, three types of features, namely, time, frequency, and time-frequency domain features are extracted. Then, wrapper-based Support Vector Machine-Recursive Feature Elimination (SVM-RFE) along with correlation bias reduction is used for feature selection. Lastly, we use SVM for categorizing positive affective attitude and negative affective attitude. Experiments show that the frontal cortex achieves the best accuracy of 98.67±2.98, 98±3.22, and 98.67±3.52 for 5-fold, 10-fold, and leave-one-subject-out (LOSO) respectively. In addition, among all the channels, Fz achieves best accuracy 90±7.81, 90.67±9.53, and 92.67±7.03 for 5-fold, 10-fold, and LOSO respectively. Subsequently, this work opens the door for implementing such a neuromarketing framework using consumer-grade devices in a real-life setting for marketers. As a result, it is evident that EEG-based neuromarketing technologies can assist brands and enterprises in forecasting future consumer preferences accurately. Hence, it will pave the way for the creation of an intelligent marketing assistive system for neuromarketing applications in future.},
}
@article {pmid35594216,
year = {2022},
author = {Chen, X and Hu, N and Gao, X},
title = {Development of a Brain-Computer Interface-Based Symbol Digit Modalities Test and Validation in Healthy Elderly Volunteers and Stroke Patients.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2022.3176615},
pmid = {35594216},
issn = {1558-0210},
abstract = {Standard cognitive assessment tools often involve motor or verbal responses, making them impossible for severely motor-disabled individuals. Brain-computer interfaces (BCIs) are expected to help severely motor-impaired individuals to perform cognitive assessment because BCIs can circumvent motor and verbal requirements. Currently, the field of research to develop cognitive tasks based on BCI is still in its nascent stage and needs further development. This study explored the possibility of developing a BCI version of symbol digit modalities test (BCI-SDMT). Steady-state visual evoked potential (SSVEP) was adopted to build the BCI and a 9-target SSVEP-BCI was realized to send examinees' responses. A training-free algorithm (i.e., filter bank canonical correlation analysis) was used for SSVEP identification. Thus, examinees are able to start the proposed BCI-SDMT immediately. Eighty-nine healthy elderly volunteers and 9 stroke patients were enrolled to validate the technical feasibility of the developed BCI-SDMT. For all participants, the average recognition accuracies of the developed BCI and BCI-SDMT were 93.89±8.48% and 92.58±10.52%, respectively, were considerably above the chance level (i.e., 11.11%). These results indicated that both healthy elderly volunteers and stroke patients could elicit sufficient SSVEPs to control the BCI. Furthermore, patient use of the developed BCI-SDMT was unaffected by the presence of motor impairment. They could understand instructions, pair numbers with specific symbols, and send commands using the BCI. The proposed BCI-SDMT can be used as a complement to the existing versions of the SDMT and has the potential to evaluate cognitive abilities in individuals with severe motor disabilities.},
}
@article {pmid35594208,
year = {2022},
author = {An, H and Nason-Tomaszewski, SR and Lim, J and Kwon, K and Willsey, MS and Patil, PG and Kim, HS and Sylvester, D and Chestek, CA and Blaauw, D},
title = {A Power-Efficient Brain-Machine Interface System with a Sub-mW Feature Extraction and Decoding ASIC Demonstrated in Nonhuman Primates.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2022.3175926},
pmid = {35594208},
issn = {1940-9990},
abstract = {Intracortical brain-machine interfaces have shown promise for restoring function to people with paralysis, but their translation to portable and implantable devices is hindered by their high power consumption. Recent devices have drastically reduced power consumption compared to standard experimental brain-machine interfaces, but still require wired or wireless connections to computing hardware for feature extraction and inference. Here, we introduce a Neural Recording And Decoding (NeuRAD) application specific integrated circuit (ASIC) in 180 nm CMOS that can extract neural spiking features and predict two-dimensional behaviors in real-time. To reduce amplifier and feature extraction power consumption, the NeuRAD has a hardware accelerator for extracting spiking band power (SBP) from intracortical spiking signals and includes an M0 processor with a fixed-point Matrix Acceleration Unit (MAU) for efficient and flexible decoding. We validated device functionality by recording SBP from a nonhuman primate implanted with a Utah microelectrode array and predicting the one- and two-dimensional finger movements the monkey was attempting to execute in closed-loop using a steady-state Kalman filter (SSKF). Using the NeuRAD's real-time predictions, the monkey achieved 100 % success rate and 0.82 s mean target acquisition time to control one-dimensional finger movements using just 581 μW. To predict two-dimensional finger movements, the NeuRAD consumed 588 μW to enable the monkey to achieve a 96 % success rate and 2.4 s mean acquisition time. By employing SBP, ASIC brain-machine interfaces can close the gap to enable fully implantable therapies for people with paralysis.},
}
@article {pmid35591103,
year = {2022},
author = {Masud, U and Saeed, T and Akram, F and Malaikah, H and Akbar, A},
title = {Unmanned Aerial Vehicle for Laser Based Biomedical Sensor Development and Examination of Device Trajectory.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {9},
pages = {},
doi = {10.3390/s22093413},
pmid = {35591103},
issn = {1424-8220},
abstract = {Controller design and signal processing for the control of air-vehicles have gained extreme importance while interacting with humans to form a brain-computer interface. This is because fewer commands need to be mapped into multiple controls. For our anticipated biomedical sensor for breath analysis, it is mandatory to provide medication to the patients on an urgent basis. To address this increasingly tense situation in terms of emergencies, we plan to design an unmanned vehicle that can aid spontaneously to monitor the person's health, and help the physician spontaneously during the rescue mission. Simultaneously, that must be done in such a computationally efficient algorithm that the minimum amount of energy resources are consumed. For this purpose, we resort to an unmanned logistic air-vehicle which flies from the medical centre to the affected person. After obtaining restricted permission from the regional administration, numerous challenges are identified for this design. The device is able to lift a weight of 2 kg successfully which is required for most emergency medications, while choosing the smallest distance to the destination with the GPS. By recording the movement of the vehicle in numerous directions, the results deviate to a maximum of 2% from theoretical investigations. In this way, our biomedical sensor provides critical information to the physician, who is able to provide medication to the patient urgently. On account of reasonable supply of medicines to the destination in terms of weight and time, this experimentation has been rendered satisfactory by the relevant physicians in the vicinity.},
}
@article {pmid35591021,
year = {2022},
author = {Värbu, K and Muhammad, N and Muhammad, Y},
title = {Past, Present, and Future of EEG-Based BCI Applications.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {9},
pages = {},
doi = {10.3390/s22093331},
pmid = {35591021},
issn = {1424-8220},
abstract = {An electroencephalography (EEG)-based brain-computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed.},
}
@article {pmid35590938,
year = {2022},
author = {Topic, A and Russo, M and Stella, M and Saric, M},
title = {Emotion Recognition Using a Reduced Set of EEG Channels Based on Holographic Feature Maps.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {9},
pages = {},
doi = {10.3390/s22093248},
pmid = {35590938},
issn = {1424-8220},
support = {KK.01.1.1.01//European Regional Development Fund - the Competitiveness and Cohesion Operational Programme/ ; },
abstract = {An important function of the construction of the Brain-Computer Interface (BCI) device is the development of a model that is able to recognize emotions from electroencephalogram (EEG) signals. Research in this area is very challenging because the EEG signal is non-stationary, non-linear, and contains a lot of noise due to artifacts caused by muscle activity and poor electrode contact. EEG signals are recorded with non-invasive wearable devices using a large number of electrodes, which increase the dimensionality and, thereby, also the computational complexity of EEG data. It also reduces the level of comfort of the subjects. This paper implements our holographic features, investigates electrode selection, and uses the most relevant channels to maximize model accuracy. The ReliefF and Neighborhood Component Analysis (NCA) methods were used to select the optimal electrodes. Verification was performed on four publicly available datasets. Our holographic feature maps were constructed using computer-generated holography (CGH) based on the values of signal characteristics displayed in space. The resulting 2D maps are the input to the Convolutional Neural Network (CNN), which serves as a feature extraction method. This methodology uses a reduced set of electrodes, which are different between men and women, and obtains state-of-the-art results in a three-dimensional emotional space. The experimental results show that the channel selection methods improve emotion recognition rates significantly with an accuracy of 90.76% for valence, 92.92% for arousal, and 92.97% for dominance.},
}
@article {pmid35590823,
year = {2022},
author = {Zapała, D and Augustynowicz, P and Tokovarov, M},
title = {Recognition of Attentional States in VR Environment: An fNIRS Study.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {9},
pages = {},
doi = {10.3390/s22093133},
pmid = {35590823},
issn = {1424-8220},
support = {CPP-2021/10/4//Cortivision sp. z o.o./ ; },
abstract = {An improvement in ecological validity is one of the significant challenges for 21st-century neuroscience. At the same time, the study of neurocognitive processes in real-life situations requires good control of all variables relevant to the results. One possible solution that combines the capability of creating realistic experimental scenarios with adequate control of the test environment is virtual reality. Our goal was to develop an integrative research workspace involving a CW-fNIRS and head-mounted-display (HMD) technology dedicated to offline and online cognitive experiments. We designed an experimental study in a repeated-measures model on a group of BCI-naïve participants to verify our assumptions. The procedure included a 3D environment-adapted variant of the classic n-back task (2-back version). Tasks were divided into offline (calibration) and online (feedback) sessions. In both sessions, the signal was recorded during the cognitive task for within-group comparisons of changes in oxy-Hb concentration in the regions of interest (the dorsolateral prefrontal cortex-DLPFC and middle frontal gyrus-MFG). In the online session, the recorded signal changes were translated into real-time feedback. We hypothesized that it would be possible to obtain significantly higher than the level-of-chance threshold classification accuracy for the enhanced attention engagement (2-back task) vs. relaxed state in both conditions. Additionally, we measured participants' subjective experiences of the BCI control in terms of satisfaction. Our results confirmed hypotheses regarding the offline condition. In accordance with the hypotheses, combining fNIRS and HMD technologies enables the effective transfer of experimental cognitive procedures to a controlled VR environment. This opens the new possibility of creating more ecologically valid studies and training procedures.},
}
@article {pmid35589391,
year = {2022},
author = {Rubin, DB and Hosman, T and Kelemen, JN and Kapitonava, A and Willett, FR and Coughlin, BF and Halgren, E and Kimchi, EY and Williams, ZM and Simeral, JD and Hochberg, LR and Cash, SS},
title = {Learned motor patterns are replayed in human motor cortex during sleep.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.2074-21.2022},
pmid = {35589391},
issn = {1529-2401},
abstract = {Consolidation of memory is believed to involve offline replay of neural activity. While amply demonstrated in rodents, evidence for replay in humans, particularly regarding motor memory, is less compelling. To determine whether replay occurs after motor learning, we sought to record from motor cortex during a novel motor task and subsequent overnight sleep. A 36-year-old man with tetraplegia secondary to cervical spinal cord injury enrolled in the ongoing BrainGate brain-computer interface pilot clinical trial had two 96-channel intracortical microelectrode arrays placed chronically into left pre-central gyrus (PCG). Single- and multi-unit activity was recorded while he played a color/sound sequence matching memory game. Intended movements were decoded from motor cortical neuronal activity by a real-time steady-state Kalman filter that allowed the participant to control a neurally driven cursor on the screen. Intracortical neural activity from PCG and 2-lead scalp EEG were recorded overnight as he slept. When decoded using the same steady-state Kalman filter parameters, intracortical neural signals recorded overnight replayed the target sequence from the memory game at intervals throughout at a frequency significantly greater than expected by chance. Replay events occurred at speeds ranging from one to four times as fast as initial task execution and were most frequently observed during slow-wave sleep. These results demonstrate that recent visuomotor skill acquisition in humans may be accompanied by replay of the corresponding motor cortex neural activity during sleep.Significance Statement:Within cortex, the acquisition of information is often followed by the offline recapitulation of specific sequences of neural firing. Replay of recent activity is enriched during sleep and may support the consolidation of learning and memory. Using an intracortical brain computer interface (iBCI), we recorded and decoded activity from motor cortex as a human research participant performed a novel motor task. By decoding neural activity throughout subsequent sleep, we find that neural sequences underlying the recently practiced motor task are repeated throughout the night, providing direct evidence of replay in human motor cortex during sleep. This approach, using an optimized BCI decoder to characterize neural activity during sleep, provides a framework for future studies exploring replay, learning, and memory.},
}
@article {pmid35588679,
year = {2022},
author = {Gao, Y and Sun, X and Meng, M and Zhang, Y},
title = {EEG emotion recognition based on enhanced SPD matrix and manifold dimensionality reduction.},
journal = {Computers in biology and medicine},
volume = {146},
number = {},
pages = {105606},
doi = {10.1016/j.compbiomed.2022.105606},
pmid = {35588679},
issn = {1879-0534},
abstract = {Recently, Riemannian geometry-based pattern recognition has been widely employed to brain computer interface (BCI) researches, providing new idea for emotion recognition based on electroencephalogram (EEG) signals. Although the symmetric positive definite (SPD) matrix manifold constructed from the traditional covariance matrix contains large amount of spatial information, these methods do not perform well to classify and recognize emotions, and the high dimensionality problem still unsolved. Therefore, this paper proposes a new strategy for EEG emotion recognition utilizing Riemannian geometry with the aim of achieving better classification performance. The emotional EEG signals of 32 healthy subjects were from an open-source dataset (DEAP). The wavelet packets were first applied to extract the time-frequency features of the EEG signals, and then the features were used to construct the enhanced SPD matrix. A supervised dimensionality reduction algorithm was then designed on the Riemannian manifold to reduce the high dimensionality of the SPD matrices, gather samples of the same labels together, and separate samples of different labels as much as possible. Finally, the samples were mapped to the tangent space, and the K-nearest neighbors (KNN), Random Forest (RF) and Support Vector Machine (SVM) method were employed for classification. The proposed method achieved an average accuracy of 91.86%, 91.84% on the valence and arousal recognition tasks. Furthermore, we also obtained the superior accuracy of 86.71% on the four-class recognition task, demonstrated the superiority over state-of-the-art emotion recognition methods.},
}
@article {pmid35588044,
year = {2022},
author = {Qian, S and Kumar, P and Testai, FD},
title = {Bilirubin Encephalopathy.},
journal = {Current neurology and neuroscience reports},
volume = {},
number = {},
pages = {},
pmid = {35588044},
issn = {1534-6293},
abstract = {PURPOSE OF REVIEW: Hyperbilirubinemia is commonly seen in neonates. Though hyperbilirubinemia is typically asymptomatic, severe elevation of bilirubin levels can lead to acute bilirubin encephalopathy and progress to kernicterus spectrum disorder, a chronic condition characterized by hearing loss, extrapyramidal dysfunction, ophthalmoplegia, and enamel hypoplasia. Epidemiological data show that the implementation of universal pre-discharge bilirubin screening programs has reduced the rates of hyperbilirubinemia-associated complications. However, acute bilirubin encephalopathy and kernicterus spectrum disorder are still particularly common in low- and middle-income countries.<br><br>RECENT FINDINGS: The understanding of the genetic and biochemical processes that increase the susceptibility of defined anatomical areas of the central nervous system to the deleterious effects of bilirubin may facilitate the development of effective treatments for acute bilirubin encephalopathy and kernicterus spectrum disorder. Scoring systems are available for the diagnosis and severity grading of these conditions. The treatment of hyperbilirubinemia in newborns relies on the use of phototherapy and exchange transfusion. However, novel therapeutic options including deep brain stimulation, brain-computer interface, and stem cell transplantation may alleviate the heavy disease burden associated with kernicterus spectrum disorder. Despite improved screening and treatment options, the prevalence of acute bilirubin encephalopathy and kernicterus spectrum disorder remains elevated in low- and middle-income countries. The continued presence and associated long-term disability of these conditions warrant further research to improve their prevention and management.},
}
@article {pmid35390744,
year = {2022},
author = {Soni, S and Seal, A and Yazidi, A and Krejcar, O},
title = {Graphical representation learning-based approach for automatic classification of electroencephalogram signals in depression.},
journal = {Computers in biology and medicine},
volume = {145},
number = {},
pages = {105420},
doi = {10.1016/j.compbiomed.2022.105420},
pmid = {35390744},
issn = {1879-0534},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Depression/diagnosis ; *Depressive Disorder, Major/diagnosis ; Electroencephalography ; Humans ; },
abstract = {Depression is a major depressive disorder characterized by persistent sadness and a sense of worthlessness, as well as a loss of interest in pleasurable activities, which leads to a variety of physical and emotional problems. It is a worldwide illness that affects millions of people and should be detected at an early stage to prevent negative effects on an individual's life. Electroencephalogram (EEG) is a non-invasive technique for detecting depression that analyses brain signals to determine the current mental state of depressed subjects. In this study, we propose a method for automatic feature extraction to detect depression by first constructing a graph from the dataset where the nodes represent the subjects in the dataset and where the edge weights obtained using the Euclidean distance reflect the relationship between them. The Node2vec algorithmic framework is then used to compute feature representations for nodes in a graph in the form of node embeddings ensuring that similar nodes in the graph remain near in the embedding. These node embeddings act as useful features which can be directly used by classification algorithms to determine whether a subject is depressed thus reducing the effort required for manual handcrafted feature extraction. To combine the features collected from the multiple channels of the EEG data, the method proposes three types of fusion methods: graph-level fusion, feature-level fusion, and decision-level fusion. The proposed method is tested on three publicly available datasets with 3, 20, and 128 channels, respectively, and compared to five state-of-the-art methods. The results show that the proposed method detects depression effectively with a peak accuracy of 0.933 in decision-level fusion, which is the highest among the state-of-the-art methods.},
}
@article {pmid35584066,
year = {2022},
author = {Xiao, J and He, Y and Yu, T and Pan, J and Xie, Q and Cao, C and Zheng, H and Huang, W and Gu, Z and Yu, Z and Li, Y},
title = {Towards Assessment of Sound Localization in Disorders of Consciousness Using a Hybrid Audiovisual Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2022.3176354},
pmid = {35584066},
issn = {1558-0210},
abstract = {Behavioral assessment of sound localization in the Coma Recovery Scale-Revised (CRS-R) poses a significant challenge due to motor disability in patients with disorders of consciousness (DOC). Brain-computer interfaces (BCIs), which can directly detect brain activities related to external stimuli, may thus provide an approach to assess DOC patients without the need for any physical behavior. In this study, a novel audiovisual BCI system was developed to simulate sound localization evaluation in CRS-R. Specifically, there were two alternatively flashed buttons on the left and right sides of the graphical user interface, one of which was randomly chosen as the target. The auditory stimuli of bell sounds were simultaneously presented by the ipsilateral loudspeaker during the flashing of the target button, which prompted patients to selectively attend to the target button. The recorded electroencephalography data were analyzed in real time to detect event-related potentials evoked by the target and further to determine whether the target was attended to or not. A significant BCI accuracy for a patient implied that he/she had sound localization. Among eighteen patients, eleven and four showed sound localization in the BCI and CRS-R, respectively. Furthermore, all patients showing sound localization in the CRS-R were among those detected by our BCI. The other seven patients who had no sound localization behavior in CRS-R were identified by the BCI assessment, and three of them showed improvements in the second CRS-R assessment after the BCI experiment. Thus, the proposed BCI system is promising for assisting the assessment of sound localization and improving the clinical diagnosis of DOC patients.},
}
@article {pmid35580764,
year = {2022},
author = {Oliveira, LJC and Amorim, LC and Megid, TBC and de Resende, CAA and Mano, MS},
title = {Gene expression signatures in early Breast Cancer: better together with clinicopathological features.},
journal = {Critical reviews in oncology/hematology},
volume = {},
number = {},
pages = {103708},
doi = {10.1016/j.critrevonc.2022.103708},
pmid = {35580764},
issn = {1879-0461},
abstract = {Breast cancer (BC) is a highly heterogeneous disease, characterized by a variety of subtypes with distinct biological, molecular, and clinical behavior. Standard clinicopathological and tumor biology information (as assessed by gene expression signatures-GES), have provided enhanced prognostic and predictive information in both node-negative(N0) and positive(N+), hormonal receptor positive/human epidermal growth factor 2 negative (HR+/HER2-) early breast cancer (EBC). Herein, we comprehensively review the clinical data of 5 commonly used GES, namely, Oncotype DX(ODX)®; MammaPrint (MP)®; Prosigna®; Breast Cancer Index (BCI)® and Endopredict® - with sections specifically addressing the role of GES in special histologic subtypes, premenopausal women, late recurrence and adjuvant treatment de-escalation.},
}
@article {pmid35580046,
year = {2022},
author = {Lei, Y and Fei, P and Song, B and Shi, W and Luo, C and Luo, D and Li, D and Chen, W and Zheng, J},
title = {A loosened gating mechanism of RIG-I leads to autoimmune disorders.},
journal = {Nucleic acids research},
volume = {},
number = {},
pages = {},
doi = {10.1093/nar/gkac361},
pmid = {35580046},
issn = {1362-4962},
support = {//Shanghai Municipal Science and Technology/ ; 81971538//National Natural Science Foundation of China/ ; PJ20190001389//Shanghai PuJiang Talent/ ; LZ19H300001//Natural Science Foundation of Zhejiang Province/ ; NMRC/OFIRG/0075/2018//National Medical Research Council/ ; },
abstract = {DDX58 encodes RIG-I, a cytosolic RNA sensor that ensures immune surveillance of nonself RNAs. Individuals with RIG-IE510V and RIG-IQ517H mutations have increased susceptibility to Singleton-Merten syndrome (SMS) defects, resulting in tissue-specific (mild) and classic (severe) phenotypes. The coupling between RNA recognition and conformational changes is central to RIG-I RNA proofreading, but the molecular determinants leading to dissociated disease phenotypes remain unknown. Herein, we employed hydrogen/deuterium exchange mass spectrometry (HDX-MS) and single molecule magnetic tweezers (MT) to precisely examine how subtle conformational changes in the helicase insertion domain (HEL2i) promote impaired ATPase and erroneous RNA proofreading activities. We showed that the mutations cause a loosened latch-gate engagement in apo RIG-I, which in turn gradually dampens its self RNA (Cap2 moiety:m7G cap and N1-2-2'-O-methylation RNA) proofreading ability, leading to increased immunopathy. These results reveal HEL2i as a unique checkpoint directing two specialized functions, i.e. stabilizing the CARD2-HEL2i interface and gating the helicase from incoming self RNAs; thus, these findings add new insights into the role of HEL2i in the control of antiviral innate immunity and autoimmunity diseases.},
}
@article {pmid35396325,
year = {2022},
author = {Liza, K and Ray, S},
title = {Local Interactions between Steady-State Visually Evoked Potentials at Nearby Flickering Frequencies.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {42},
number = {19},
pages = {3965-3974},
pmid = {35396325},
issn = {1529-2401},
support = {/WT_/Wellcome Trust/United Kingdom ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Photic Stimulation/methods ; },
abstract = {Steady-state visually evoked potentials (SSVEPs) are widely used to index top-down cognitive processing in human electroencephalogram (EEG) studies. Typically, two stimuli flickering at different temporal frequencies (TFs) are presented, each producing a distinct response in the EEG at its flicker frequency. However, how SSVEP responses in EEGs are modulated in the presence of a competing flickering stimulus just because of sensory interactions is not well understood. We have previously shown in local field potentials (LFPs) recorded from awake monkeys that when two overlapping full-screen gratings are counterphased at different TFs, there is an asymmetric SSVEP response suppression, with greater suppression from lower TFs, which further depends on the relative orientations of the gratings (stronger suppression and asymmetry for parallel compared with orthogonal gratings). Here, we first confirmed these effects in both male and female human EEG recordings. Then, we mapped the response suppression of one stimulus (target) by a competing stimulus (mask) over a much wider range than the previous study. Surprisingly, we found that the suppression was not stronger at low frequencies in general, but systematically varied depending on the target TF, indicating local interactions between the two competing stimuli. These results were confirmed in both human EEG and monkey LFP and electrocorticogram (ECoG) data. Our results show that sensory interactions between multiple SSVEPs are more complex than shown previously and are influenced by both local and global factors, underscoring the need to cautiously interpret the results of studies involving SSVEP paradigms.SIGNIFICANCE STATEMENT Steady-state visually evoked potentials (SSVEPs) are extensively used in human cognitive studies and brain-computer interfacing applications where multiple stimuli flickering at distinct frequencies are concurrently presented in the visual field. We recently characterized interactions between competing flickering stimuli in animal recordings and found that stimuli flickering slowly produce larger suppression. Here, we confirmed these in human EEGs, and further characterized the interactions by using a much wider range of target and competing (mask) frequencies in both human EEGs and invasive animal recordings. These revealed a new "local" component, whereby the suppression increased when competing stimuli flickered at nearby frequencies. Our results highlight the complexity of sensory interactions among multiple SSVEPs and underscore the need to cautiously interpret studies involving SSVEP paradigms.},
}
@article {pmid35576911,
year = {2022},
author = {Flint, RD and Li, Y and Wang, P and Vaidya, M and Barry, A and Ghassemi, M and Tomic, G and Brkic, N and Ripley, D and Liu, C and Kamper, D and Do, A and Slutzky, MW},
title = {Noninvasively recorded high-gamma signals improve synchrony of force feedback in a novel neurorehabilitation brain-machine interface for brain injury.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ac7004},
pmid = {35576911},
issn = {1741-2552},
abstract = {OBJECTIVE: Brain injury is the leading cause of long-term disability worldwide, often resulting in impaired hand function. Brain-machine interfaces (BMIs) offer a potential way to improve hand function. BMIs often target replacing lost function, but may also be employed in neurorehabilitation (nrBMI) by facilitating neural plasticity and functional recovery. Here, we report a novel nrBMI capable of acquiring high-γ (70-115 Hz) information through a unique post-TBI hemicraniectomy window model, and delivering sensory feedback that is synchronized with, and proportional to, intended grasp force.<br><br>APPROACH: We developed the nrBMI to use electroencephalogram recorded over a hemicraniectomy (hEEG) in individuals with traumatic brain injury (TBI). The nrBMI empowered users to exert continuous, proportional control of applied force, and provided continuous force feedback. We report the results of an initial testing group of three human participants with TBI, along with a control group of three skull- and motor-intact volunteers.<br><br>MAIN RESULTS: All participants controlled the nrBMI successfully, with high initial success rates (2 of 6 participants) or performance that improved over time (4 of 6 participants). We observed high-γ modulation with force intent in hEEG but not skull-intact EEG. Most significantly, we found that high-γ control significantly improved the timing synchronization between neural modulation onset and nrBMI output/haptic feedback (compared to low-frequency nrBMI control).<br><br>SIGNIFICANCE: These proof-of-concept results show that high-γ nrBMIs can be used by individuals with impaired ability to control force (without immediately resorting to invasive signals like ECoG). Of note, the nrBMI includes a parameter to change the fraction of control shared between decoded intent and volitional force, to adjust for recovery progress. The improved synchrony between neural modulations and force control for high-γ signals is potentially important for maximizing the ability of nrBMIs to induce plasticity in neural circuits. Inducing plasticity is critical to functional recovery after brain injury.},
}
@article {pmid35576428,
year = {2022},
author = {Zhou, Y and Yang, B and Guan, C},
title = {Task-Related Component Analysis Combining Paired Character Decoding for Miniature Asymmetric Visual Evoked Potentials.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2022.3175307},
pmid = {35576428},
issn = {1558-0210},
abstract = {Brain-computer interface (BCI) technology based on event-related potentials (ERP) of electroencephalography (EEG) is widely used in daily life and medical treatment. However, the research of identifying the miniature and more informative asymmetric visual evoked potentials (aVEPs), which belongs to ERP, needs further exploration. Herein, a task-related component analysis combining paired character decoding (TRCA-PCD) method, which can enhance reproducibility of aVEPs in multiple trials and strengthen the features of different samples, was designed to realize fast decoding of aVEPs. The BCI performance and the influence of repetition times between the TRCA-PCD method, the discriminative canonical pattern matching (DCPM) method and traditional task-related component analysis (TRCA) method were compared using a 32-class aVEPs dataset recorded from 32 subjects. The highest average recognition accuracy and information transfer rate (ITR) of TRCA-PCD after parameter selection were 70.37 ± 2.49% (DCPM: 64.91 ± 2.81%, TRCA: 44.01 ± 3.25%) with the peak value of 97.92% and 28.90 ± 3.83 bits/min (DCPM: 21.29 ± 3.35 bits/min, TRCA: 11.54 ± 2.81 bits/min) with the peak value of 94.55 bits/min respectively. Statistical analysis indicated that the highest average recognition rate could be obtained when the repetition time was six, and the highest ITR could be obtained when the repetition time was one. Overall, the results verified the effectiveness and superiority of TRCA-PCD in recognition of aVEPs and provided a reference for parameter selection. Therefore, the TRCA-PCD method can promote the further application of aVEPs in the BCI speller field.},
}
@article {pmid35574291,
year = {2022},
author = {Loizidou, P and Rios, E and Marttini, A and Keluo-Udeke, O and Soetedjo, J and Belay, J and Perifanos, K and Pouratian, N and Speier, W},
title = {Extending Brain-Computer Interface Access with a Multilingual Language Model in the P300 Speller.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {9},
number = {1},
pages = {36-48},
doi = {10.1080/2326263x.2021.1993426},
pmid = {35574291},
issn = {2326-263X},
abstract = {Brain-computer interfaces (BCI) such as the P300 speller have the potential to restore communication to advanced-stage neuromuscular disease patients. Research has improved typing speed and accuracy through innovations including the use of language models. While significant advances have been made, implementations have largely been restricted to a single language, primarily English. It is unclear whether these improvements would extend to other languages that present potential technical hurdles due to different alphabets and grammatical structures. Here, we adapt a language model-based classifier designed for English to two other languages, Spanish and Greek, to demonstrate the generalizability of these methods. Online experimental trials with 30 healthy native English, Spanish, and Greek speakers showed no significant difference between performances using the different versions of the system (66.20 vs. 61.97 vs. 60.89 bits/minute). Extending these methods across languages allows for expanding access to BCI systems to other populations, particularly in the developing world.},
}
@article {pmid35573313,
year = {2022},
author = {Fan, C and Hu, J and Huang, S and Peng, Y and Kwong, S},
title = {EEG-TNet: An End-To-End Brain Computer Interface Framework for Mental Workload Estimation.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {869522},
doi = {10.3389/fnins.2022.869522},
pmid = {35573313},
issn = {1662-4548},
abstract = {The mental workload (MWL) of different occupational groups' workers is the main and direct factor of unsafe behavior, which may cause serious accidents. One of the new and useful technologies to estimate MWL is the Brain computer interface (BCI) based on EEG signals, which is regarded as the gold standard of cognitive status. However, estimation systems involving handcrafted EEG features are time-consuming and unsuitable to apply in real-time. The purpose of this study was to propose an end-to-end BCI framework for MWL estimation. First, a new automated data preprocessing method was proposed to remove the artifact without human interference. Then a new neural network structure named EEG-TNet was designed to extract both the temporal and frequency information from the original EEG. Furthermore, two types of experiments and ablation studies were performed to prove the effectiveness of this model. In the subject-dependent experiment, the estimation accuracy of dual-task estimation (No task vs. TASK) and triple-task estimation (Lo vs. Mi vs. Hi) reached 99.82 and 99.21%, respectively. In contrast, the accuracy of different tasks reached 82.78 and 66.83% in subject-independent experiments. Additionally, the ablation studies proved that preprocessing method and network structure had significant contributions to estimation MWL. The proposed method is convenient without any human intervention and outperforms other related studies, which becomes an effective way to reduce human factor risks.},
}
@article {pmid35573306,
year = {2022},
author = {Vasko, JL and Aume, L and Tamrakar, S and Colachis, SCI and Dunlap, CF and Rich, A and Meyers, EC and Gabrieli, D and Friedenberg, DA},
title = {Increasing Robustness of Brain-Computer Interfaces Through Automatic Detection and Removal of Corrupted Input Signals.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {858377},
doi = {10.3389/fnins.2022.858377},
pmid = {35573306},
issn = {1662-4548},
abstract = {For brain-computer interfaces (BCIs) to be viable for long-term daily usage, they must be able to quickly identify and adapt to signal disruptions. Furthermore, the detection and mitigation steps need to occur automatically and without the need for user intervention while also being computationally tractable for the low-power hardware that will be used in a deployed BCI system. Here, we focus on disruptions that are likely to occur during chronic use that cause some recording channels to fail but leave the remaining channels unaffected. In these cases, the algorithm that translates recorded neural activity into actions, the neural decoder, should seamlessly identify and adjust to the altered neural signals with minimal inconvenience to the user. First, we introduce an adapted statistical process control (SPC) method that automatically identifies disrupted channels so that both decoding algorithms can be adjusted, and technicians can be alerted. Next, after identifying corrupted channels, we demonstrate the automated and rapid removal of channels from a neural network decoder using a masking approach that does not change the decoding architecture, making it amenable for transfer learning. Finally, using transfer and unsupervised learning techniques, we update the model weights to adjust for the corrupted channels without requiring the user to collect additional calibration data. We demonstrate with both real and simulated neural data that our approach can maintain high-performance while simultaneously minimizing computation time and data storage requirements. This framework is invisible to the user but can dramatically increase BCI robustness and usability.},
}
@article {pmid35571721,
year = {2022},
author = {Li, L and Sun, N},
title = {Attention-Based DSC-ConvLSTM for Multiclass Motor Imagery Classification.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {8187009},
doi = {10.1155/2022/8187009},
pmid = {35571721},
issn = {1687-5273},
abstract = {With the rapid development of deep learning, researchers have gradually applied it to motor imagery brain computer interface (MI-BCI) and initially demonstrated its advantages over traditional machine learning. However, its application still faces many challenges, and the recognition rate of electroencephalogram (EEG) is still the bottleneck restricting the development of MI-BCI. In order to improve the accuracy of EEG classification, a DSC-ConvLSTM model based on the attention mechanism is proposed for the multi-classification of motor imagery EEG signals. To address the problem of the small sample size of well-labeled and accurate EEG data, the preprocessing uses sliding windows for data augmentation, and the average prediction loss of each sliding window is used as the final prediction loss for that trial. This not only increases the training sample size and is beneficial to train complex neural network models, but also the network no longer extracts the global features of the whole trial so as to avoid learning the difference features among trials, which can effectively eliminate the influence of individual specificity. In the aspect of feature extraction and classification, the overall network structure is designed according to the characteristics of the EEG signals in this paper. Firstly, depth separable convolution (DSC) is used to extract spatial features of EEG signals. On the one hand, this reduces the number of parameters and improves the response speed of the system. On the other hand, the network structure we designed is more conducive to extract directly the direct extraction of spatial features of EEG signals. Secondly, the internal structure of the Long Short-Term Memory (LSTM) unit is improved by using convolution and attention mechanism, and a novel bidirectional convolution LSTM (ConvLSTM) structure is proposed by comparing the effects of embedding convolution and attention mechanism in the input and different gates, respectively. In the ConvLSTM module, the convolutional structure is only introduced into the input-to-state transition, while the gates still remain the original fully connected mechanism, and the attention mechanism is introduced into the input to further improve the overall decoding performance of the model. This bidirectional ConvLSTM extracts the time-domain features of EEG signals and integrates the feature extraction capability of the CNN and the sequence processing capability of LSTM. The experimental results show that the average classification accuracy of the model reaches 73.7% and 92.6% on two datasets, BCI Competition IV Dataset 2a and High Gamma Dataset, respectively, which proves the robustness and effectiveness of the model we proposed. It can be seen that the model in this paper can deeply excavate significant EEG features from the original EEG signals, show good performance in different subjects and different datasets, and improve the influence of individual variability on the classification performance, which is of practical significance for promoting the development of brain-computer interface technology towards a practical and marketable direction.},
}
@article {pmid35571670,
year = {2022},
author = {Zeng, C and Zhang, J},
title = {A narrative review of five multigenetic assays in breast cancer.},
journal = {Translational cancer research},
volume = {11},
number = {4},
pages = {897-907},
doi = {10.21037/tcr-21-1920},
pmid = {35571670},
issn = {2219-6803},
abstract = {Background and Objective: Breast cancer is a highly heterogeneous disease. Its incidence rate is increasing year by year and the mortality rate is the highest in female malignant tumors. Even patients with the same clinical stage and pathological grade have different response to treatment and postoperative recurrence risk. Although the prognosis of breast cancer in China has been gradually improved, there is still a certain gap compared with the 5-year survival rate as high as 89% in developed countries. In recent years, with the continuous enrichment of molecular sequencing data of breast cancer, gene detection technology has important reference value in prognosis judgement and guiding treatment of early breast cancer. This article reviews the current application and latest progress of genetic tests in comprehensive treatment for breast cancer, with a view to promote the precise treatment of breast cancer in clinical practice.<br><br>Methods: We conducted searches using the MeSH terms 'breast neoplasms' and 'genetic testing' in the PubMed databases from root to 22 January 2021. We conducted an additional search in the National Comprehensive Cancer Network (NCCN) and American Society of Clinical Oncology (ASCO) guidelines to obtain additional information. The search was limited to English, Dutch, French and German articles and research involving humans. Out of the references screened, 51 articles were found eligible for inclusion finally.<br><br>Key Content and Findings: The article reviews the mechanisms and clinical trials of five genetic tests including Oncotype Dx, Mammaprint, Endopredict, mRNA expression of 50 genes (PAM50) and breast cancer index (BCI) in comprehensive treatment for breast cancer. All these tools have been proved to have prognosis value, but only two of them, Oncotype Dx and Mammaprint, are recommended as predictive tools for chemotherapy by National Comprehensive Cancer Network (NCCN) and the American Society of Clinical Oncology (ASCO).<br><br>Conclusions: In order to promote the comprehensive treatment of breast cancer to "precision" and "individualization" for further development, people have extensively researched on multigene testing technology represented by Oncotype Dx, Mammaprint, Endopredict and mRNA expression of 50 genes (PAM50) and breast cancer index (BCI). Each of these five tools has its advantages and limitation, which must be weighed in a wise application.},
}
@article {pmid35569239,
year = {2022},
author = {Zarei, A and Mohammadzadeh Asl, B},
title = {Classification of code-modulated visual evoked potentials using adaptive modified covariance beamformer and EEG signals.},
journal = {Computer methods and programs in biomedicine},
volume = {221},
number = {},
pages = {106859},
doi = {10.1016/j.cmpb.2022.106859},
pmid = {35569239},
issn = {1872-7565},
abstract = {OBJECTIVE: In general, brain computer interface (BCI) studies based on code-modulated Visual Evoked Potentials (c-VEP) use m-sequences to decode EEG responses to visual stimuli. BCI systems based on the c-VEP paradigm can simultaneously present a large number of commands, which results in a significantly high information transfer rate (ITR). Spatiotemporal beamforming (STB) is one of the commonly used approaches in c-VEP-based BCI systems.<br><br>APPROACH: In the current work, a novel STB-based technique is proposed to detect the gazed targets. The proposed method improves the performance of conventional STB-based techniques by providing a robust estimation of the covariance matrix in short stimulation times. Different user parameter-free methods, including the convex combination (CC), the general linear combination (GLC), and the modified versions of these techniques, are used to estimate a reliable and robust covariance matrix when a small number of repetitions are available.<br><br>MAIN RESULTS: The stimulus presentation rate of 120 Hz is used to assess the performance of the proposed structures. Our proposed methods improved the classification accuracy by an average of 20% compared to the conventional STB method at the shortest stimulation time. The proposed method achieves an average ITR of 157.07 bits/min by using only two repetitions of the m-sequences.<br><br>SIGNIFICANCE: The results show that our proposed methods perform significantly better than the conventional STB technique in all stimulation times.},
}
@article {pmid35565569,
year = {2022},
author = {Wang, J and Qian, L and Wang, S and Shi, L and Wang, Z},
title = {Directional Preference in Avian Midbrain Saliency Computing Nucleus Reflects a Well-Designed Receptive Field Structure.},
journal = {Animals : an open access journal from MDPI},
volume = {12},
number = {9},
pages = {},
doi = {10.3390/ani12091143},
pmid = {35565569},
issn = {2076-2615},
abstract = {Neurons responding sensitively to motions in several rather than all directions have been identified in many sensory systems. Although this directional preference has been demonstrated by previous studies to exist in the isthmi pars magnocellularis (Imc) of pigeon (Columba livia), which plays a key role in the midbrain saliency computing network, the dynamic response characteristics and the physiological basis underlying this phenomenon are unclear. Herein, dots moving in 16 directions and a biologically plausible computational model were used. We found that pigeon Imc's significant responses for objects moving in preferred directions benefit the long response duration and high instantaneous firing rate. Furthermore, the receptive field structures predicted by a computational model, which captures the actual directional tuning curves, agree with the real data collected from population Imc units. These results suggested that directional preference in Imc may be internally prebuilt by elongating the vertical axis of the receptive field, making predators attack from the dorsal-ventral direction and conspecifics flying away in the ventral-dorsal direction, more salient for avians, which is of great ecological and physiological significance for survival.},
}
@article {pmid35558735,
year = {2022},
author = {Lopez-Bernal, D and Balderas, D and Ponce, P and Molina, A},
title = {A State-of-the-Art Review of EEG-Based Imagined Speech Decoding.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {867281},
doi = {10.3389/fnhum.2022.867281},
pmid = {35558735},
issn = {1662-5161},
abstract = {Currently, the most used method to measure brain activity under a non-invasive procedure is the electroencephalogram (EEG). This is because of its high temporal resolution, ease of use, and safety. These signals can be used under a Brain Computer Interface (BCI) framework, which can be implemented to provide a new communication channel to people that are unable to speak due to motor disabilities or other neurological diseases. Nevertheless, EEG-based BCI systems have presented challenges to be implemented in real life situations for imagined speech recognition due to the difficulty to interpret EEG signals because of their low signal-to-noise ratio (SNR). As consequence, in order to help the researcher make a wise decision when approaching this problem, we offer a review article that sums the main findings of the most relevant studies on this subject since 2009. This review focuses mainly on the pre-processing, feature extraction, and classification techniques used by several authors, as well as the target vocabulary. Furthermore, we propose ideas that may be useful for future work in order to achieve a practical application of EEG-based BCI systems toward imagined speech decoding.},
}
@article {pmid35552976,
year = {2022},
author = {He, W and Yang, J and Gao, M and Liu, H and Li, J and Hu, J and Zhang, Y and Zhong, G and Li, K and Dong, W and Huang, H and Lin, T and Huang, J},
title = {Pelvic reconstruction and lateral prostate capsule sparing techniques improve early continence of robot-assisted radical cystectomy with orthotopic ileal neobladder.},
journal = {International urology and nephrology},
volume = {},
number = {},
pages = {},
pmid = {35552976},
issn = {1573-2584},
support = {2018YFA0902803//National Key Research and Development Program of China/ ; 2017YFC1308600//National Key Research and Development Program of China/ ; 81825016//National Natural Science Foundation of China/ ; 81802530//National Natural Science Foundation of China/ ; 81830082//National Natural Science Foundation of China/ ; 81672395//National Natural Science Foundation of China/ ; 81871945//National Natural Science Foundation of China/ ; 81772719//National Natural Science Foundation of China/ ; 81772728//National Natural Science Foundation of China/ ; 2072639//National Natural Science Foundation of China/ ; 91740119//National Natural Science Foundation of China/ ; 81472381//National Natural Science Foundation of China/ ; 81972385//National Natural Science Foundation of China/ ; 82173266//National Natural Science Foundation of China/ ; 81802552//National Natural Science Foundation of China/ ; 2020A1515010815//the Key Areas Research and Development Program of Guangdong/ ; 2018B010109006//the Key Areas Research and Development Program of Guangdong/ ; 2017A020215072//the Key Areas Research and Development Program of Guangdong/ ; 202002030388//Science and Technology Planning Project of Guangdong Province/ ; 201803010049//Science and Technology Planning Project of Guangdong Province/ ; 2017B020227007//Science and Technology Planning Project of Guangdong Province/ ; 201704020097//Science and Technology Planning Project of Guangdong Province/ ; 2020B1111170006//Guangdong Clinical Research Center for Urological Diseases/ ; YXQH201812//Yixian Youth project of Sun Yat-sen Memorial Hospital/ ; 19ykzd21//Young Teacher Training Funding of Sun Yat-sen University/ ; 19ykpy121//Young Teacher Training Funding of Sun Yat-sen University/ ; 201904010004//Science and Technology Program of Guangzhou, China/ ; 2018A030313545//Natural Science Foundation of Guangdong Province, China/ ; },
abstract = {PURPOSE: To evaluate urinary outcomes of pelvic construction and lateral capsule sparing techniques in robot-assisted radical cystectomy with orthotopic ileal neobladder (RARC-OIN).<br><br>METHODS: A total of 107 male patients who underwent RARC-OIN during January 2017 and February 2021 in Sun Yat-sen Memorial Hospital were analyzed retrospectively. Standard RARC-OIN with or without nerve sparing technique was performed in 44 patients (standard group), lateral prostate capsule sparing technique was performed in 20 patients (LCS group), combined pelvic reconstruction (CPR) technique including anterior suspension and posterior reconstruction were performed in 43 patients (CPR group). The urinary function was assessed by the use of pads and the Bladder Cancer Index (BCI). Continence was defined as the use of 0-1 pad during daytime or night-time.<br><br>RESULTS: There was no statistical difference between the three groups regarding demographic, perioperative, and pathological data. Continence rates were 6.8, 50.0 and 34.9% for daytime, 4.6, 40.0 and 32.6% for night-time in the standard group, LCS group and CPR group at 1 month post-operation, respectively. Continence rates were 34.1, 80.0 and 69.8% for daytime, 27.3, 75.0 and 65.1% for night-time in the standard group, LCS group and CPR group at 3 month post-operation, respectively. No statistically significant difference was observed in the daytime and night-time continence rates at 12 months.<br><br>CONCLUSIONS: Lateral capsule-sparing and combined pelvic reconstruction techniques are feasible to improve early daytime and night-time continence rates in RARC with orthotopic neobladder.<br><br>CLINICAL TRIAL REGISTRATION: The trial registration number: ChiCTR2100047606.},
}
@article {pmid35552154,
year = {2022},
author = {Dag, I and Dui, LG and Ferrante, S and Pedrocchi, A and Antonietti, A},
title = {Leveraging Deep Learning Techniques to Improve P300-Based Brain Computer Interfaces.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2022.3174771},
pmid = {35552154},
issn = {2168-2208},
abstract = {Brain-Computer Interface (BCI) has become an established technology to interconnect a human brain and an external device. One of the most popular protocols for BCI is based on the extraction of the so-called P300 wave from electroencephalography (EEG) recordings. P300 wave is an event-related potential with a latency of 300 ms after the onset of a rare stimulus. In this paper, we used deep learning architectures, namely convolutional neural networks (CNNs), to improve P300-based BCIs. We propose a novel BCI classifier, called P3CNET, that improved P300 classification accuracy performances of the best state-of-the-art classifier. In addition, we explored pre-processing and training choices that improved the usability of BCI systems. For the pre-processing of EEG data, we explored the optimal signal interval that would improve classification accuracies. Then, we explored the minimum number of calibration sessions to balance higher accuracy and shorter calibration time. To improve the explainability of deep learning architectures, we analyzed the saliency maps of the input EEG signal leading to a correct P300 classification, and we observed that the elimination of less informative electrode channels from the data did not result in better accuracy. All the methodologies and explorations were performed and validated on two different CNN classifiers, demonstrating the generalizability of the obtained results. Finally, we showed the advantages given by transfer learning when using the proposed novel architecture on other P300 datasets. The presented architectures and practical suggestions can be used by BCI practitioners to improve its effectiveness.},
}
@article {pmid35550813,
year = {2022},
author = {Lubianiker, N and Paret, C and Dayan, P and Hendler, T},
title = {Neurofeedback through the lens of reinforcement learning.},
journal = {Trends in neurosciences},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.tins.2022.03.008},
pmid = {35550813},
issn = {1878-108X},
abstract = {Despite decades of experimental and clinical practice, the neuropsychological mechanisms underlying neurofeedback (NF) training remain obscure. NF is a unique form of reinforcement learning (RL) task, during which participants are provided with rewarding feedback regarding desired changes in neural patterns. However, key RL considerations - including choices during practice, prediction errors, credit-assignment problems, or the exploration-exploitation tradeoff - have infrequently been considered in the context of NF. We offer an RL-based framework for NF, describing different internal states, actions, and rewards in common NF protocols, thus fashioning new proposals for characterizing, predicting, and hastening the course of learning. In this way we hope to advance current understanding of neural regulation via NF, and ultimately to promote its effectiveness, personalization, and clinical utility.},
}
@article {pmid35548781,
year = {2022},
author = {Kumar, A and Gao, L and Li, J and Ma, J and Fu, J and Gu, X and Mahmoud, SS and Fang, Q},
title = {Error-Related Negativity-Based Robot-Assisted Stroke Rehabilitation System: Design and Proof-of-Concept.},
journal = {Frontiers in neurorobotics},
volume = {16},
number = {},
pages = {837119},
doi = {10.3389/fnbot.2022.837119},
pmid = {35548781},
issn = {1662-5218},
abstract = {Conventional rehabilitation systems typically execute a fixed set of programs that most motor-impaired stroke patients undergo. In these systems, the brain, which is embodied in the body, is often left out. Including the brains of stroke patients in the control loop of a rehabilitation system can be worthwhile as the system can be tailored to each participant and, thus, be more effective. Here, we propose a novel brain-computer interface (BCI)-based robot-assisted stroke rehabilitation system (RASRS), which takes inputs from the patient's intrinsic feedback mechanism to adapt the assistance level of the RASRS. The proposed system will utilize the patients' consciousness about their performance decoded through their error-related negativity signals. As a proof-of-concept, we experimented on 12 healthy people in which we recorded their electroencephalogram (EEG) signals while performing a standard rehabilitation exercise. We set the performance requirements beforehand and observed participants' neural responses when they failed/met the set requirements and found a statistically significant (p < 0.05) difference in their neural responses in the two conditions. The feasibility of the proposed BCI-based RASRS was demonstrated through a use-case description with a timing diagram and meeting the crucial requirements for developing the proposed rehabilitation system. The use of a patient's intrinsic feedback mechanism will have significant implications for the development of human-in-the-loop stroke rehabilitation systems.},
}
@article {pmid35547770,
year = {2022},
author = {Zhang, Y and Lu, S and Huang, S and Yu, Z and Xia, T and Li, M and Yang, C and Mao, Y and Xu, B and Wang, L and Xu, L and Shi, J and Zhu, X and Zhu, S and Zhang, S and Qian, H and Hu, Y and Li, W and Tu, Y and Wu, W},
title = {Optic chiasmatic potential by endoscopically implanted skull base microinvasive biosensor: a brain-machine interface approach for anterior visual pathway assessment.},
journal = {Theranostics},
volume = {12},
number = {7},
pages = {3273-3287},
doi = {10.7150/thno.71164},
pmid = {35547770},
issn = {1838-7640},
abstract = {Background: Visually evoked potential (VEP) is widely used to detect optic neuropathy in basic research and clinical practice. Traditionally, VEP is recorded non-invasively from the surface of the skull over the visual cortex. However, its trace amplitude is highly variable, largely due to intracranial modulation and artifacts. Therefore, a safe test with a strong and stable signal is highly desirable to assess optic nerve function, particularly in neurosurgical settings and animal experiments. Methods: Minimally invasive trans-sphenoidal endoscopic recording of optic chiasmatic potential (OCP) was carried out with a titanium screw implanted onto the sphenoid bone beneath the optic chiasm in the goat, whose sphenoidal anatomy is more human-like than non-human primates. Results: The implantation procedure was swift (within 30 min) and did not cause any detectable abnormality in fetching/moving behaviors, skull CT scans and ophthalmic tests after surgery. Compared with traditional VEP, the amplitude of OCP was 5-10 times stronger, more sensitive to weak light stimulus and its subtle changes, and was more repeatable, even under extremely low general anesthesia. Moreover, the OCP signal relied on ipsilateral light stimulation, and was abolished immediately after complete optic nerve (ON) transection. Through proof-of-concept experiments, we demonstrated several potential applications of the OCP device: (1) real-time detector of ON function, (2) detector of region-biased retinal sensitivity, and (3) therapeutic electrical stimulator for the optic nerve with low and thus safe excitation threshold. Conclusions: OCP developed in this study will be valuable for both vision research and clinical practice. This study also provides a safe endoscopic approach to implant skull base brain-machine interface, and a feasible in vivo testbed (goat) for evaluating safety and efficacy of skull base brain-machine interface.},
}
@article {pmid35546894,
year = {2022},
author = {Yang, J and Liu, L and Yu, H and Ma, Z and Shen, T},
title = {Multi-Hierarchical Fusion to Capture the Latent Invariance for Calibration-Free Brain-Computer Interfaces.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {824471},
doi = {10.3389/fnins.2022.824471},
pmid = {35546894},
issn = {1662-4548},
abstract = {Brain-computer interfaces (BCI) based motor imagery (MI) has become a research hotspot for establishing a flexible communication channel for patients with apoplexy or degenerative pathologies. Accurate decoding of motor imagery electroencephalography (MI-EEG) signals, while essential for effective BCI systems, is still challenging due to the significant noise inherent in the EEG signals and the lack of informative correlation between the signals and brain activities. The application of deep learning for EEG feature representation has been rarely investigated, nevertheless bringing improvements to the performance of motor imagery classification. This paper proposes a deep learning decoding method based on multi-hierarchical representation fusion (MHRF) on MI-EEG. It consists of a concurrent framework constructed of bidirectional LSTM (Bi-LSTM) and convolutional neural network (CNN) to fully capture the contextual correlations of MI-EEG and the spectral feature. Also, the stacked sparse autoencoder (SSAE) is employed to concentrate these two domain features into a high-level representation for cross-session and subject training guidance. The experimental analysis demonstrated the efficacy and practicality of the proposed approach using a public dataset from BCI competition IV and a private one collected by our MI task. The proposed approach can serve as a robust and competitive method to improve inter-session and inter-subject transferability, adding anticipation and prospective thoughts to the practical implementation of a calibration-free BCI system.},
}
@article {pmid35546879,
year = {2022},
author = {Shishkin, SL},
title = {Active Brain-Computer Interfacing for Healthy Users.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {859887},
doi = {10.3389/fnins.2022.859887},
pmid = {35546879},
issn = {1662-4548},
}
@article {pmid35405146,
year = {2022},
author = {Campos-Arteaga, G and Araneda, A and Ruiz, S and Rodríguez, E and Sitaram, R},
title = {Classifying brain states and pupillary responses associated with the processing of old and new information.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {176},
number = {},
pages = {129-141},
doi = {10.1016/j.ijpsycho.2022.04.004},
pmid = {35405146},
issn = {1872-7697},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Machine Learning ; Mental Recall ; },
abstract = {Memory retrieval of consolidated memories has been extensively studied using "old-new tasks", meaning tasks in which participants are instructed to discriminate between stimuli they have experienced before and new ones. Significant differences in the neural processing of old and new elements have been demonstrated using different techniques, such as electroencephalography and pupillometry. In this work, using the data from a previously published study (Campos-Arteaga, Forcato et al. 2020), we investigated whether machine learning methods can classify, based on single trials, the brain activity and pupil responses associated with the processing of old and new information. Specifically, we used the EEG and pupillary information of 39 participants who completed an associative recall old-new task in which they had to discriminate between previously seen or new pictures and, for the old ones, to recall an associated word. Our analyses corroborated the differences in neural processing of old and new items reported in previous studies. Based on these results, we hypothesized that the application of machine learning methods would allow an optimal classification of old and new conditions. Using a Windowed Means approach (WM) and two different machine learning algorithms - Logistic Regression (WM-LR) and Linear Discriminant Analysis (WM-LDA) - mean classification performances of 0.75 and 0.74 (AUC) were achieved when EEG and pupillary signals were combined to train the models, respectively. In both cases, when the EEG and pupillary data were merged, the performance was significantly better than when they were used separately. In addition, our results showed similar classification performances when fused classification models (i.e., models created with the concatenated information of 38 participants) were applied to individuals whose EEG and pupillary information was not considered for the model training. Similar results were found when alternative preprocessing methods were used. Taken together, these findings show that it is possible to classify the neurophysiological activity associated with the processing of experienced and new stimuli using machine learning techniques. Future research is needed to determine how this knowledge might have potential implications for memory research and clinical practice.},
}
@article {pmid35533644,
year = {2022},
author = {Andersen, RA and Aflalo, T},
title = {Preserved cortical somatotopic and motor representations in tetraplegic humans.},
journal = {Current opinion in neurobiology},
volume = {74},
number = {},
pages = {102547},
doi = {10.1016/j.conb.2022.102547},
pmid = {35533644},
issn = {1873-6882},
abstract = {A rich literature has documented changes in cortical representations of the body in somatosensory and motor cortex. Recent clinical studies of brain-machine interfaces designed to assist paralyzed patients have afforded the opportunity to record from and stimulate human somatosensory, motor, and action-related areas of the posterior parietal cortex. These studies show considerable preserved structure in the cortical somato-motor system. Motor cortex can immediately control assistive devices, stimulation of somatosensory cortex produces sensations in an orderly somatotopic map, and the posterior parietal cortex shows a high-dimensional representation of cognitive action variables. These results are strikingly similar to what would be expected in a healthy subject, demonstrating considerable stability of adult cortex even after severe injury and despite potential plasticity-induced new activations within the same region of cortex. Clinically, these results emphasize the importance of targeting cortical areas for BMI control signals that are consistent with their normal functional role.},
}
@article {pmid35533168,
year = {2022},
author = {Szlawski, J and Feleppa, T and Mohan, A and Wong, YT and Lowery, AJ},
title = {A model for assessing the electromagnetic safety of an inductively coupled, modular brain-machine interface (May 2022).},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2022.3173682},
pmid = {35533168},
issn = {1558-0210},
abstract = {Brain-Machine Interfaces (BMI) offer the potential to modulate dysfunctional neurological networks by electrically stimulating the cerebral cortex via chronically-implanted microelectrodes. Wireless transmitters worn by BMI recipients must operate within electromagnetic emission and tissue heating limits, such as those prescribed by the IEEE and International Commission on Non-Ionizing Radiation Protection (ICNIRP), to ensure that radiofrequency emissions of BMI systems are safe. Here, we describe an approach to generating pre-compliance safety data by simulating the Specific Absorption Rate (SAR) and tissue heating of a multi-layered human head model containing a system of wireless, modular BMIs powered and controlled by an externally worn telemetry unit. We explore a number of system configurations such that our approach can be utilized for similar BMI systems, and our results provide a benchmark for the electromagnetic emissions of similar telemetry units. Our results show that the volume-averaged SAR per 10g of tissue exposed to our telemetry field complies with ICNIRP and IEEE reference levels, and that the maximum temperature increase in tissues was within permissible limits. These results were unaffected by the number of implants in the system model, and therefore we conclude that the electromagnetic emissions our BMI in any configuration are safe.},
}
@article {pmid35533152,
year = {2022},
author = {Pancholi, S and Giri, A and Jain, A and Kumar, L and Roy, S},
title = {Source Aware Deep Learning Framework for Hand Kinematic Reconstruction Using EEG Signal.},
journal = {IEEE transactions on cybernetics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TCYB.2022.3166604},
pmid = {35533152},
issn = {2168-2275},
abstract = {The ability to reconstruct the kinematic parameters of hand movement using noninvasive electroencephalography (EEG) is essential for strength and endurance augmentation using exoskeleton/exosuit. For system development, the conventional classification-based brain-computer interface (BCI) controls external devices by providing discrete control signals to the actuator. A continuous kinematic reconstruction from EEG signal is better suited for practical BCI applications. The state-of-the-art multivariable linear regression (mLR) method provides a continuous estimate of hand kinematics, achieving a maximum correlation of up to 0.67 between the measured and the estimated hand trajectory. In this work, three novel source aware deep learning models are proposed for motion trajectory prediction (MTP). In particular, multilayer perceptron (MLP), convolutional neural network-long short-term memory (CNN-LSTM), and wavelet packet decomposition (WPD) for CNN-LSTM are presented. In addition, novelty in the work includes the utilization of brain source localization (BSL) [using standardized low-resolution brain electromagnetic tomography (sLORETA)] for the reliable decoding of motor intention. The information is utilized for channel selection and accurate EEG time segment selection. The performance of the proposed models is compared with the traditionally utilized mLR technique on the reach, grasp, and lift (GAL) dataset. The effectiveness of the proposed framework is established using the Pearson correlation coefficient (PCC) and trajectory analysis. A significant improvement in the correlation coefficient is observed when compared with the state-of-the-art mLR model. Our work bridges the gap between the control and the actuator block, enabling real-time BCI implementation.},
}
@article {pmid35530739,
year = {2021},
author = {Zhu, L and Hu, Q and Yang, J and Zhang, J and Xu, P and Ying, N},
title = {EEG Signal Classification Using Manifold Learning and Matrix-Variate Gaussian Model.},
journal = {Computational intelligence and neuroscience},
volume = {2021},
number = {},
pages = {6668859},
doi = {10.1155/2021/6668859},
pmid = {35530739},
issn = {1687-5273},
abstract = {In brain-computer interface (BCI), feature extraction is the key to the accuracy of recognition. There is important local structural information in the EEG signals, which is effective for classification; and this locality of EEG features not only exists in the spatial channel position but also exists in the frequency domain. In order to retain sufficient spatial structure and frequency information, we use one-versus-rest filter bank common spatial patterns (OVR-FBCSP) to preprocess the data and extract preliminary features. On this basis, we conduct research and discussion on feature extraction methods. One-dimensional feature extraction methods like linear discriminant analysis (LDA) may destroy this kind of structural information. Traditional manifold learning methods or two-dimensional feature extraction methods cannot extract both types of information at the same time. We introduced the bilinear structure and matrix-variate Gaussian model into two-dimensional discriminant locality preserving projection (2DDLPP) algorithm and decompose EEG signals into spatial and spectral parts. Afterwards, the most discriminative features were selected through a weight calculation method. We tested the method on BCI competition data sets 2a, data sets IIIa, and data sets collected by our laboratory, and the results were expressed in terms of recognition accuracy. The cross-validation results were 75.69%, 70.46%, and 54.49%, respectively. The average recognition accuracy of new method is improved by 7.14%, 7.38%, 4.86%, and 3.8% compared to those of LDA, two-dimensional linear discriminant analysis (2DLDA), discriminant locality property projections (DLPP), and 2DDLPP, respectively. Therefore, we consider that the proposed method is effective for EEG classification.},
}
@article {pmid35530175,
year = {2022},
author = {Kondo, T and Saito, R and Sato, Y and Sato, K and Uchida, A and Yoshino-Saito, K and Shinozaki, M and Tashiro, S and Nagoshi, N and Nakamura, M and Ushiba, J and Okano, H},
title = {Treadmill Training for Common Marmoset to Strengthen Corticospinal Connections After Thoracic Contusion Spinal Cord Injury.},
journal = {Frontiers in cellular neuroscience},
volume = {16},
number = {},
pages = {858562},
doi = {10.3389/fncel.2022.858562},
pmid = {35530175},
issn = {1662-5102},
abstract = {Spinal cord injury (SCI) leads to locomotor dysfunction. Locomotor rehabilitation promotes the recovery of stepping ability in lower mammals, but it has limited efficacy in humans with a severe SCI. To explain this discrepancy between different species, a nonhuman primate rehabilitation model with a severe SCI would be useful. In this study, we developed a rehabilitation model of paraplegia caused by a severe traumatic SCI in a nonhuman primate, common marmoset (Callithrix jacchus). The locomotor rating scale for marmosets was developed to accurately assess the recovery of locomotor functions in marmosets. All animals showed flaccid paralysis of the hindlimb after a thoracic contusive SCI, but the trained group showed significant locomotor recovery. Kinematic analysis revealed significantly improved hindlimb stepping patterns in trained marmosets. Furthermore, intracortical microstimulation (ICMS) of the motor cortex evoked the hindlimb muscles in the trained group, suggesting the reconnection between supraspinal input and the lumbosacral network. Because rehabilitation may be combined with regenerative interventions such as medicine or cell therapy, this primate model can be used as a preclinical test of therapies that can be used in human clinical trials.},
}
@article {pmid35529778,
year = {2022},
author = {Chandler, JA and Van der Loos, KI and Boehnke, S and Beaudry, JS and Buchman, DZ and Illes, J},
title = {Brain Computer Interfaces and Communication Disabilities: Ethical, Legal, and Social Aspects of Decoding Speech From the Brain.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {841035},
doi = {10.3389/fnhum.2022.841035},
pmid = {35529778},
issn = {1662-5161},
abstract = {A brain-computer interface technology that can decode the neural signals associated with attempted but unarticulated speech could offer a future efficient means of communication for people with severe motor impairments. Recent demonstrations have validated this approach. Here we assume that it will be possible in future to decode imagined (i.e., attempted but unarticulated) speech in people with severe motor impairments, and we consider the characteristics that could maximize the social utility of a BCI for communication. As a social interaction, communication involves the needs and goals of both speaker and listener, particularly in contexts that have significant potential consequences. We explore three high-consequence legal situations in which neurally-decoded speech could have implications: Testimony, where decoded speech is used as evidence; Consent and Capacity, where it may be used as a means of agency and participation such as consent to medical treatment; and Harm, where such communications may be networked or may cause harm to others. We then illustrate how design choices might impact the social and legal acceptability of these technologies.},
}
@article {pmid35529775,
year = {2022},
author = {Klee, D and Memmott, T and Smedemark-Margulies, N and Celik, B and Erdogmus, D and Oken, BS},
title = {Target-Related Alpha Attenuation in a Brain-Computer Interface Rapid Serial Visual Presentation Calibration.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {882557},
doi = {10.3389/fnhum.2022.882557},
pmid = {35529775},
issn = {1662-5161},
abstract = {This study evaluated the feasibility of using occipitoparietal alpha activity to drive target/non-target classification in a brain-computer interface (BCI) for communication. EEG data were collected from 12 participants who completed BCI Rapid Serial Visual Presentation (RSVP) calibrations at two different presentation rates: 1 and 4 Hz. Attention-related changes in posterior alpha activity were compared to two event-related potentials (ERPs): N200 and P300. Machine learning approaches evaluated target/non-target classification accuracy using alpha activity. Results indicated significant alpha attenuation following target letters at both 1 and 4 Hz presentation rates, though this effect was significantly reduced in the 4 Hz condition. Target-related alpha attenuation was not correlated with coincident N200 or P300 target effects. Classification using posterior alpha activity was above chance and benefitted from individualized tuning procedures. These findings suggest that target-related posterior alpha attenuation is detectable in a BCI RSVP calibration and that this signal could be leveraged in machine learning algorithms used for RSVP or comparable attention-based BCI paradigms.},
}
@article {pmid35527849,
year = {2022},
author = {Wang, CH and Tsai, KY},
title = {Optimization of machine learning method combined with brain-computer interface rehabilitation system.},
journal = {Journal of physical therapy science},
volume = {34},
number = {5},
pages = {379-385},
doi = {10.1589/jpts.34.379},
pmid = {35527849},
issn = {0915-5287},
abstract = {[Purpose] Stroke patients are unable to move on their own and must be rehabilitated to allow the nervous system to trigger and restore its function. Traditional practice is to use electrode caps to extract brain wave features and combine them with assistive devices. However, there are problems that the electrode cap is not easy to wear, and the potential recognition is not good, and different extraction methods will affect the accuracy of the Brain-Computer Interfaces (BCI), which still has room for improvement. [Participants and Methods] The brainwave headphones used in this experiment do not must a conductive gel to get a good EEG for neural induction and drive the upper limb rehabilitation robot. Next, 8 stroke patients and 200 normal participants were invited for a 4-week rehabilitation training. The effectiveness of the training was determined using Fast Fourier Transform (FFT), Magnitude squared coherence (MSC) feature extraction methods, and five machine learning techniques that induced flicker frequencies. [Results] The results show that the optimal steady-state visual evoked flicker frequency is 6 Hz, and the identification rate of FFT is about 5.2% higher than that of the MSC method. Using an optimized model for different feature extraction methods can improve the recognition rate by 1.3%-9.1%. [Conclusion] The images based on Fugl-Meyer Assessment (FMA), Modified Ashworth Scale (MAS) index improvement, and functional Magnetic Resonance Imaging (fMRI) show that the sensory region of brain movement has become a concentrated activation phenomenon. Besides strengthening the feature extraction method also lets the elbow has an obvious recovery effect.},
}
@article {pmid35171745,
year = {2022},
author = {DePass, M and Falaki, A and Quessy, S and Dancause, N and Cos, I},
title = {A machine learning approach to characterize sequential movement-related states in premotor and motor cortices.},
journal = {Journal of neurophysiology},
volume = {127},
number = {5},
pages = {1348-1362},
doi = {10.1152/jn.00368.2021},
pmid = {35171745},
issn = {1522-1598},
support = {945539//Fet Flagship (HBP SGA3)/ ; 389886//Gouvernement du Canada | Canadian Institutes of Health Research (CIHR)/ ; PID2019-105093GB-100//Ministerio de Economía, Industria y Competitividad, Gobierno de España (MINECO)/ ; },
abstract = {Nonhuman primate (NHP) movement kinematics have been decoded from spikes and local field potentials (LFPs) recorded during motor tasks. However, the potential of LFPs to provide network-like characterizations of neural dynamics during planning and execution of sequential movements requires further exploration. Is the aggregate nature of LFPs suitable to construct informative brain state descriptors of movement preparation and execution? To investigate this, we developed a framework to process LFPs based on machine-learning classifiers and analyzed LFP from a primate, implanted with several microelectrode arrays covering the premotor cortex in both hemispheres and the primary motor cortex on one side. The monkey performed a reach-to-grasp task, consisting of five consecutive states, starting from rest until a rewarding target (food) was attained. We use this five-state task to characterize neural activity within eight frequency bands, using spectral amplitude and pairwise correlations across electrodes as features. Our results show that we could best distinguish all five movement-related states using the highest frequency band (200-500 Hz), yielding an 87% accuracy with spectral amplitude, and 60% with pairwise electrode correlation. Further analyses characterized each movement-related state, showing differential neuronal population activity at above-γ frequencies during the various stages of movement. Furthermore, the topological distribution for the high-frequency LFPs allowed for a highly significant set of pairwise correlations, strongly suggesting a concerted distribution of movement planning and execution function is distributed across premotor and primary motor cortices in a specific fashion, and is most significant in the low ripple (100-150 Hz), high ripple (150-200 Hz), and multiunit frequency bands. In summary, our results show that the concerted use of novel machine-learning techniques with coarse grained queue broad signals such as LFPs may be successfully used to track and decode fine movement aspects involving preparation, reach, grasp, and reward retrieval across several brain regions.NEW &amp; NOTEWORTHY Local field potentials (LFPs), despite lower spatial resolution compared to single-neuron recordings, can be used with machine learning classifiers to decode sequential movements involving motor preparation, execution, and reward retrieval. Our results revealed heterogeneity of neural activity on small spatial scales, further evidencing the utility of micro-electrode array recordings for complex movement decoding. With further advancement, high-dimensional LFPs may become the gold standard for brain-computer interfaces such as neural prostheses in the near future.},
}
@article {pmid35525171,
year = {2022},
author = {Mahmood, M and Kim, N and Mahmood, M and Kim, H and Kim, H and Rodeheaver, N and Sang, M and Yu, KJ and Yeo, WH},
title = {VR-enabled portable brain-computer interfaces via wireless soft bioelectronics.},
journal = {Biosensors &amp; bioelectronics},
volume = {210},
number = {},
pages = {114333},
doi = {10.1016/j.bios.2022.114333},
pmid = {35525171},
issn = {1873-4235},
abstract = {Noninvasive, wearable brain-computer interfaces (BCI) find limited use due to their obtrusive nature and low information. Currently available portable BCI systems are limited by device rigidity, bulky form factors, and gel-based skin-contact electrodes - and therefore more prone to noise and motion artifacts. Here, we introduce virtual reality (VR)-enabled split-eye asynchronous stimulus (SEAS) allowing a target to present different stimuli to either eye. This results in unique asynchronous stimulus patterns measurable with as few as four EEG electrodes, as demonstrated with improved wireless soft electronics for portable BCI. This VR-embedded SEAS paradigm demonstrates potential for improved throughput with a greater number of unique stimuli. A wearable soft platform featuring dry needle electrodes and shielded stretchable interconnects enables high throughput decoding of steady-state visually evoked potentials (SSVEP) for a text spelling interface. A combination of skin-conformal electrodes and soft materials offers high-quality recordings of SSVEP with minimal motion artifacts, validated by comparing the performance with a conventional wearable system. A deep-learning algorithm provides real-time classification, with an accuracy of 78.93% for 0.8 s and 91.73% for 2 s with 33 classes from nine human subjects, allowing for a successful demonstration of VR text spelling and navigation of a real-world environment. With as few as only four data recording channels, the system demonstrates a highly competitive information transfer rate (243.6 bit/min). Collectively, the VR-enabled soft system offers unique advantages in wireless, real-time monitoring of brain signals for portable BCI, neurological rehabilitation, and disease diagnosis.},
}
@article {pmid35524069,
year = {2022},
author = {Irmer, C and Volkenstein, S and Dazert, S and Neumann, A},
title = {The bone conduction implant BONEBRIDGE increases quality of life and social life satisfaction.},
journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery},
volume = {},
number = {},
pages = {},
pmid = {35524069},
issn = {1434-4726},
abstract = {PURPOSE: Transcutaneous active bone conduction hearing aids represent an alternative approach to middle ear surgery and conventional hearing aids for patients with conductive or mixed hearing loss. The aim of this study was to determine quality of life, subjective hearing experience and patients' satisfaction after implantation of a bone conduction hearing aid.<br><br>METHODS: This monocentric and retrospective study included twelve adult patients who received a bone conduction hearing aid (Bonebridge, MedEL) consisting of an extracorporeal audio processor and a bone conduction implant (BCI) between 2013 and 2017. On average 40 months after implantation, the patients were asked to answer three questionnaires regarding quality of life (AqoL-8D), self-reported auditory disability (SSQ-12-B) and user's satisfaction (APSQ) after implantation of the Bonebridge (BB). A descriptive statistical analysis of the questionnaires followed.<br><br>RESULTS: 12 patients aged 26-85 years (sex: m = 7, w = 5) were recruited. The quality of life of all patients after implantation of the BB (AqoL 8D) averaged an overall utility score of 0.76 (SD ± 0.17). The mean for 'speech hearing' in the SSQ-12-B was + 2.43 (SD ± 2.03), + 1.94 (SD ± 1.48) for 'spatial hearing' and + 2.28 (SD ± 2.32) for 'qualities of hearing'. 11 out of 12 patients reported an improvement in their overall hearing. The APSQ score for the subsection 'wearing comfort' was 3.50 (SD ± 0.87), 'social life' attained a mean of 4.17 (SD ± 1.06). The 'device inconveniences' reached 4.02 (SD ± 0.71) and 'usability' of the device was measured at 4.23 (SD ± 1.06). The average wearing time of the audio processor in the cohort was 11 h per day, with 8 of 12 patients reporting the maximum length of 12 h per day.<br><br>CONCLUSION: BB implantation results in a gain in the perceived quality of life (AqoL 8D). The SSQ-12-B shows an improvement in subjective hearing. According to the APSQ, it can be assumed that the BB audio processor, although in an extracorporeal position, is rated as a useful instrument with positive impact on social life. The majority stated that they had subjectively benefited from BB implantation and that there were no significant physical or sensory limitations after implantation.},
}
@article {pmid35523564,
year = {2022},
author = {Yang, M and Jung, TP and Han, J and Xu, M and Ming, D},
title = {[A review of researches on decoding algorithms of steady-state visual evoked potentials].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {39},
number = {2},
pages = {416-425},
doi = {10.7507/1001-5515.202111066},
pmid = {35523564},
issn = {1001-5515},
abstract = {Brain-computer interface (BCI) systems based on steady-state visual evoked potential (SSVEP) have become one of the major paradigms in BCI research due to their high signal-to-noise ratio and short training time required by users. Fast and accurate decoding of SSVEP features is a crucial step in SSVEP-BCI research. However, the current researches lack a systematic overview of SSVEP decoding algorithms and analyses of the connections and differences between them, so it is difficult for researchers to choose the optimum algorithm under different situations. To address this problem, this paper focuses on the progress of SSVEP decoding algorithms in recent years and divides them into two categories-trained and non-trained-based on whether training data are needed. This paper also explains the fundamental theories and application scopes of decoding algorithms such as canonical correlation analysis (CCA), task-related component analysis (TRCA) and the extended algorithms, concludes the commonly used strategies for processing decoding algorithms, and discusses the challenges and opportunities in this field in the end.},
}
@article {pmid35523563,
year = {2022},
author = {Luo, J and Ding, P and Gong, A and Tian, G and Xu, H and Zhao, L and Fu, Y},
title = {[Applications, industrial transformation and commercial value of brain-computer interface technology].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {39},
number = {2},
pages = {405-415},
doi = {10.7507/1001-5515.202108068},
pmid = {35523563},
issn = {1001-5515},
abstract = {Brain-computer interface (BCI) is a revolutionary human-computer interaction technology, which includes both BCI that can output instructions directly from the brain to external devices or machines without relying on the peripheral nerve and muscle system, and BCI that bypasses the peripheral nerve and muscle system and inputs electrical, magnetic, acoustic and optical stimuli or neural feedback directly to the brain from external devices or machines. With the development of BCI technology, it has potential application not only in medical field, but also in non-medical fields, such as education, military, finance, entertainment, smart home and so on. At present, there is little literature on the relevant application of BCI technology, the current situation of BCI industrialization at home and abroad and its commercial value. Therefore, this paper expounds and discusses the above contents, which are expected to provide valuable information for the public and organizations, BCI researchers, BCI industry translators and salespeople, and improve the cognitive level of BCI technology, further promote the application and industrial transformation of BCI technology and enhance the commercial value of BCI, so as to serve mankind better.},
}
@article {pmid35523129,
year = {2022},
author = {Ji, Y and Li, F and Fu, B and Li, Y and Zhou, Y and Niu, Y and Zhang, L and Chen, Y and Shi, G},
title = {Spatial-temporal Network for Fine-grained-level Emotion EEG Recognition.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ac6d7d},
pmid = {35523129},
issn = {1741-2552},
abstract = {Electroencephalogram (EEG)-based affective computing brain-computer interfaces provide the capability for machines to understand human intentions. In practice, people are more concerned with the strength of a certain emotional state over a short period of time, which was called as fine-grained-level emotion in this paper. In this study, we built a fine-grained-level emotion EEG dataset that contains two coarse-grained emotions and four corresponding fine-grained-level emotions. To fully extract the features of the EEG signals, we proposed a corresponding fine-grained emotion EEG network (FG-emotionNet) for spatial-temporal feature extraction. Each feature extraction layer is linked to raw EEG signals to alleviate overfitting and ensure that the spatial features of each scale can be extracted from the raw signals. Moreover, all previous scale features are fused before the current spatial-feature layer to enhance the scale features in the spatial block. Additionally, long short-term memory is adopted as the temporal block to extract the temporal features based on spatial features and classify the category of fine-grained emotions. Subject-dependent and cross-session experiments demonstrated that the performance of the proposed method is superior to that of the representative methods in emotion recognition and similar structure methods with proposed method.},
}
@article {pmid35523120,
year = {2022},
author = {Farabbi, A and Aloia, V and Mainardi, L},
title = {ARX-Based EEG Data Balancing for Error Potential BCI.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ac6d7f},
pmid = {35523120},
issn = {1741-2552},
abstract = {Deep learning algorithms employed in Brain Computer Interfaces (BCI) need large electroencephalographic (EEG) datasets to be trained. These datasets are usually unbalanced, particularly when Error Potential (ErrP) experiment are considered, being ErrP's epochs much rarer than Non-ErrP ones. To face the issue of unbalance of rare epochs, this paper presents a novel, data balancing methods based on ARX-modelling.<br><br>APPROACH: AutoRegressive with eXogenous input (ARX)-models are identified on the EEG data of the "Monitoring error-related potentials" dataset of the BNCI Horizon 2020 and then employed to generate new synthetic data of the minority class of ErrP epochs. The balanced dataset is used to train a classifier of Non-Errp vs. ErrP epochs based on EEGNet.<br><br>MAIN RESULTS: Compared to classical techniques (e.g.: class weights, CW) for data balancing, the new method outperforms the others in terms of resulting accuracy (i.e: ARX 91.5% vs CW 88.3%), F1-score (i.e: ARX 78.3% vs CW 73.7%) and balanced accuracy (i.e: ARX 87.0% vs CW 81.1%) and also reduces the number of false positive detection (i.e: ARX 51 vs CW 104). Moreover, the ARX-based method shows a better generalization capability of the whole model to classify and predict new data.<br><br>SIGNIFICANCE: The results obtained suggest that the proposed method can be used in BCI application for tackling the issue of data unbalance and obtain more reliable and robust performances.},
}
@article {pmid35523023,
year = {2022},
author = {Perez-Valero, E and Lopez-Gordo, MÁ and Gutiérrez, CM and Carrera-Muñoz, I and Vílchez-Carrillo, RM},
title = {A self-driven approach for multi-class discrimination in Alzheimer's disease based on wearable EEG.},
journal = {Computer methods and programs in biomedicine},
volume = {220},
number = {},
pages = {106841},
doi = {10.1016/j.cmpb.2022.106841},
pmid = {35523023},
issn = {1872-7565},
abstract = {Early detection is critical to control Alzheimer's disease (AD) progression and postpone cognitive decline. Traditional medical procedures such as magnetic resonance imaging are costly, involve long waiting lists, and require complex analysis. Alternatively, for the past years, researchers have successfully evaluated AD detection approaches based on machine learning and electroencephalography (EEG). Nonetheless, these approaches frequently rely upon manual processing or involve non-portable EEG hardware. These aspects are suboptimal regarding automated diagnosis, since they require additional personnel and hinder portability. In this work, we report the preliminary evaluation of a self-driven AD multi-class discrimination approach based on a commercial EEG acquisition system using sixteen channels. For this purpose, we recorded the EEG of three groups of participants: mild AD, mild cognitive impairment (MCI) non-AD, and controls, and we implemented a self-driven analysis pipeline to discriminate the three groups. First, we applied automated artifact rejection algorithms to the EEG recordings. Then, we extracted power, entropy, and complexity features from the preprocessed epochs. Finally, we evaluated a multi-class classification problem using a multi-layer perceptron through leave-one-subject-out cross-validation. The preliminary results that we obtained are comparable to the best in literature (0.88 F1-score), what suggests that AD can potentially be detected through a self-driven approach based on commercial EEG and machine learning. We believe this work and further research could contribute to opening the door for the detection of AD in a single consultation session, therefore reducing the costs associated to AD screening and potentially advancing medical treatment.},
}
@article {pmid35519260,
year = {2022},
author = {Wojtkiewicz, S and Bejm, K and Liebert, A},
title = {Lock-in functional near-infrared spectroscopy for measurement of the haemodynamic brain response.},
journal = {Biomedical optics express},
volume = {13},
number = {4},
pages = {1869-1887},
doi = {10.1364/BOE.448038},
pmid = {35519260},
issn = {2156-7085},
abstract = {Here we show a method of the lock-in amplifying near-infrared signals originating within a human brain. It implies using two 90-degree rotated source-detector pairs fixed on a head surface. Both pairs have a joint sensitivity region located towards the brain. A direct application of the lock-in technique on both signals results in amplifying common frequency components, e.g. related to brain cortex stimulation and attenuating the rest, including all components not related to the stimulation: e.g. pulse, instrumental and biological noise or movement artefacts. This is a self-driven method as no prior assumptions are needed and the noise model is provided by the interfering signals themselves. We show the theory (classical modified Beer-Lambert law and diffuse optical tomography approaches), the algorithm implementation and tests on a finite element mathematical model and in-vivo on healthy volunteers during visual cortex stimulation. The proposed hardware and algorithm complexity suit the entire spectrum of (continuous wave, frequency domain, time-resolved) near-infrared spectroscopy systems featuring real-time, direct, robust and low-noise brain activity registration tool. As such, this can be of special interest in optical brain computer interfaces and high reliability/stability monitors of tissue oxygenation.},
}
@article {pmid35519153,
year = {2022},
author = {Haider, S and Saleem, F and Ahmad, N and Iqbal, Q and Bashaar, M},
title = {Translation, Validation, and Psychometric Evaluation of the Diabetes Quality-of-Life Brief Clinical Inventory: The Urdu Version.},
journal = {Journal of multidisciplinary healthcare},
volume = {15},
number = {},
pages = {955-966},
doi = {10.2147/JMDH.S351330},
pmid = {35519153},
issn = {1178-2390},
abstract = {Purpose: The study is aimed to examine the psychometric properties of the Urdu version of the Diabetes Quality-of-Life Brief Clinical Inventory.<br><br>Methods: We adopted the forward-backward procedure to translate the Diabetes Quality-of-Life Brief Clinical Inventory (DQoL-BCI) into the Urdu language (lingua franca of Pakistan). The intraclass correlation (ICC) confirmed the consistency of retaining the items, and Cronbach's alpha established the test-re-test reliability. The confirmatory factor analysis (principal axis factoring extraction and oblique rotation with Kaiser normalization) validated the DQoL-BCI in Urdu.<br><br>Results: A two-time point with an interval of 2 weeks was used, and the Urdu version of DQoL-BCI was piloted accordingly. The 15-item translated version (DQoL-BCI-U) exhibited a satisfactory Cronbach's value of 0.866 (test) at week 1 and 0.850 at week 3 (re-test). Using the one-way random model with single measurements, the ICC for all 15 items exhibited coefficient values of >0.80. The Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett's Test of Sphericity revealed relationships of the data and suitability of CFA (0.899, p<0.05). Seven factors explaining the total variance of 69% were extracted. With acceptable communalities, all 15 items of DQoL-BCI-U were retained.<br><br>Conclusion: The study concludes that the translated version of DQoL-BCI-U is a valid instrument in regions, where Urdu is a communal language of communication and can examine quality-of-life issues during the typical patient-provider encounter.},
}
@article {pmid35516202,
year = {2020},
author = {Kim, YJ and Yoon, S and Cho, YH and Kim, G and Kim, HK},
title = {Paintable and writable electrodes using black conductive ink on traditional Korean paper (Hanji).},
journal = {RSC advances},
volume = {10},
number = {41},
pages = {24631-24641},
doi = {10.1039/d0ra04412a},
pmid = {35516202},
issn = {2046-2069},
abstract = {We demonstrate black conductive ink (BCI) that is writable and paintable on traditional handmade Korean paper (Hanji) for application as a high performing electrode. By optimal mixing of Ag nanowire (Ag NW) suspension and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate)(PEDOT:PSS) solution in standard charcoal-based blank ink, we synthesized BCI suitable for writing and painting on Hanji with a normal paintbrush. Due to the shear stress induced by the paintbrush bristles, the Ag NW and PEDOT:PSS mixture was uniformly coated on the porous cellulose structure of Hanji and showed a low sheet resistance of 11.7 Ohm per square even after repeated brush strokes. Moreover, the brush-painted electrodes on Hanji showed a constant resistance during tests of inner/outer bending and folding due to the outstanding flexibility of the Ag NW and PEDOT:PSS mixture that filled the porous cellulose structure of Hanji. Therefore, the pictures drawn in the BCI on Hanji exhibited a level of flexibility and conductivity sufficiently high to enable the BCI to function as an effective electrode even when the paper substrate is wrinkled or crumpled. The successful operation of the paintable interconnector and heater on Hanji indicates the high potential of the brush-painted electrodes that can be used in various social and cultural fields, including fine art, fashion, interior design, architecture, and heating industry.},
}
@article {pmid35513171,
year = {2022},
author = {Zhang, R and Zeng, Y and Tong, L and Shu, J and Lu, R and Yang, K and Li, Z and Yan, B},
title = {ERP-WGAN: A Data Augmentation Method for EEG Single-trial Detection.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {109621},
doi = {10.1016/j.jneumeth.2022.109621},
pmid = {35513171},
issn = {1872-678X},
abstract = {Brain computer interaction based on EEG presents great potential and becomes the research hotspots. However, the insufficient scale of EEG database limits the BCI system performance, especially the positive and negative sample imbalance caused by oddball paradigm. To alleviate the bottleneck problem of scarce EEG sample, we propose a data augmentation method based on generative adversarial network to improve the performance of EEG signal classification. Taking the characteristics of EEG into account in wasserstein generative adversarial networks (WGAN), the problems of model collapse and poor quality of artificial data were solved by using resting noise, smoothing and random amplitude. The quality of artificial data was comprehensively evaluated from verisimilitude, diversity and accuracy. Compared with the three artificial data methods and two data sampling methods, the proposed ERP-WGAN framework significantly improve the performance of both subject and general classifiers, especially the accuracy of general classifiers trained by less than 5 dimensional features is improved by 20-25%. Moreover, we evaluate the training sets performance with different mixing ratios of artificial and real samples. ERP-WGAN can reduced at least 73% of the real subject data and acquisition cost, which greatly saves the test cycle and research cost.},
}
@article {pmid35511858,
year = {2022},
author = {Beavers, DP and Hsieh, KL and Kitzman, DW and Kritchevsky, SB and Messier, SP and Neiberg, RH and Nicklas, BJ and Rejeski, WJ and Beavers, KM},
title = {Estimating heterogeneity of physical function treatment response to caloric restriction among older adults with obesity.},
journal = {PloS one},
volume = {17},
number = {5},
pages = {e0267779},
doi = {10.1371/journal.pone.0267779},
pmid = {35511858},
issn = {1932-6203},
abstract = {Clinical trials conventionally test aggregate mean differences and assume homogeneous variances across treatment groups. However, significant response heterogeneity may exist. The purpose of this study was to model treatment response variability using gait speed change among older adults participating in caloric restriction (CR) trials. Eight randomized controlled trials (RCTs) with five- or six-month assessments were pooled, including 749 participants randomized to CR and 594 participants randomized to non-CR (NoCR). Statistical models compared means and variances by CR assignment and exercise assignment or select subgroups, testing for treatment differences and interactions for mean changes and standard deviations. Continuous equivalents of dichotomized variables were also fit. Models used a Bayesian framework, and posterior estimates were presented as means and 95% Bayesian credible intervals (BCI). At baseline, participants were 67.7 (SD = 5.4) years, 69.8% female, and 79.2% white, with a BMI of 33.9 (4.4) kg/m2. CR participants reduced body mass [CR: -7.7 (5.8) kg vs. NoCR: -0.9 (3.5) kg] and increased gait speed [CR: +0.10 (0.16) m/s vs. NoCR: +0.07 (0.15) m/s] more than NoCR participants. There were no treatment differences in gait speed change standard deviations [CR-NoCR: -0.002 m/s (95% BCI: -0.013, 0.009)]. Significant mean interactions between CR and exercise assignment [0.037 m/s (95% BCI: 0.004, 0.070)], BMI [0.034 m/s (95% BCI: 0.003, 0.066)], and IL-6 [0.041 m/s (95% BCI: 0.009, 0.073)] were observed, while variance interactions were observed between CR and exercise assignment [-0.458 m/s (95% BCI: -0.783, -0.138)], age [-0.557 m/s (95% BCI: -0.900, -0.221)], and gait speed [-0.530 m/s (95% BCI: -1.018, -0.062)] subgroups. Caloric restriction plus exercise yielded the greatest gait speed benefit among older adults with obesity. High BMI and IL-6 subgroups also improved gait speed in response to CR. Results provide a novel statistical framework for identifying treatment heterogeneity in RCTs.},
}
@article {pmid35511845,
year = {2022},
author = {Salvatore, C and Valeriani, D and Piccialli, V and Bianchi, L},
title = {Optimized Collaborative Brain-Computer Interfaces for Enhancing Face Recognition.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2022.3173079},
pmid = {35511845},
issn = {1558-0210},
abstract = {The aim of this study is to maximize group decision performance by optimally adapting EEG confidence decoders to the group composition. We train linear support vector machines to estimate the decision confidence of human participants from their EEG activity. We then simulate groups of different size and membership by combining individual decisions using a weighted majority rule. The weights assigned to each participant in the group are chosen solving a small-dimension, mixed, integer linear programming problem, where we maximize the group performance on the training set. We therefore introduce optimized collaborative brain-computer interfaces (BCIs), where the decisions of each team member are weighted according to both the individual neural activity and the group composition. We validate this approach on a face recognition task undertaken by 10 human participants. The results show that optimal collaborative BCIs significantly enhance team performance over other BCIs, while improving fairness within the group. This research paves the way for practical applications of collaborative BCIs to realistic scenarios characterized by stable teams, where optimizing the decision policy of a single group may lead to significant long-term benefits of team dynamics.},
}
@article {pmid35509047,
year = {2022},
author = {Xu, H and Piao, L and Liu, X and Jiang, SN},
title = {Ursolic acid-enriched kudingcha extract enhances the antitumor activity of bacteria-mediated cancer immunotherapy.},
journal = {BMC complementary medicine and therapies},
volume = {22},
number = {1},
pages = {123},
pmid = {35509047},
issn = {2662-7671},
abstract = {BACKGROUND: Bacteria-mediated cancer immunotherapy (BCI) robustly stimulates the immune system and represses angiogenesis, but tumor recurrence and metastasis commonly occur after BCI. The natural product Ilex kudingcha C. J Tseng enriched with ursolic acid has anti-cancer activity and could potentially augment the therapeutic effects of BCI. The objective of the present study was to determine potential additive effects of these modalities.<br><br>METHODS: We investigated the anti-cancer activity of KDCE (Kudingcha extract) combined with S.t△ppGpp in the mice colon cancer models.<br><br>RESULTS: In the present study, KDCE combined with S.t△ppGpp BCI improved antitumor therapeutic efficacy compared to S.t△ppGpp or KDCE alone. KDCE did not prolong bacterial tumor-colonizing time, but enhanced the antiangiogenic effect of S.t△ppGpp by downregulatingVEGFR2. We speculated that KDCE-induced VEGFR2 downregulation is associated with FAK/MMP9/STAT3 axis but not AKT or ERK.<br><br>CONCLUSIONS: Ursolic acid-enriched KDCE enhances the antitumor activity of BCI, which could be mediated by VEGFR2 downregulation and subsequent suppression of angiogenesis. Therefore, combination therapy with S.t△ppGpp and KDCE is a potential cancer therapeutic strategy.},
}
@article {pmid35508113,
year = {2022},
author = {Sujatha Ravindran, A and Malaya, C and John, I and Francisco, GE and Layne, C and Contreras-Vidal, JL},
title = {Decoding Neural Activity Preceding Balance Loss During Standing with a Lower-limb Exoskeleton using an Interpretable Deep Learning Model.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ac6ca9},
pmid = {35508113},
issn = {1741-2552},
abstract = {Falls are a leading cause of death in adults 65 and older. Recent efforts to restore lower-limb function in these populations have seen an increase in the use of wearable robotic systems; however, fall prevention measures in these systems require early detection of balance loss to be effective. Prior studies have investigated whether kinematic variables contain information about an impending fall, but few have examined the potential of using electroencephalography (EEG) as a fall-predicting signal and how the brain responds to avoid a fall. To address this, we decoded neural activity in a balance perturbation task while wearing an exoskeleton. We acquired EEG, electromyography (EMG), and center of pressure (COP) data from 7 healthy participants during mechanical perturbations while standing. The timing of the perturbations was randomized in all trials. We found perturbation evoked potentials (PEP) components as early as 75-134 ms after the onset of the external perturbation, which preceded both the peak in EMG (∼ 180 ms) and the COP (∼ 350 ms). A convolutional neural network trained to predict balance perturbations from single-trial EEG had a mean F-score of 75.0 ± 4.3 %. Clustering GradCAM-based model explanations demonstrated that the model utilized components in the PEP and was not driven by artifacts. Additionally, dynamic functional connectivity results agreed with model explanations; the nodal connectivity measured using phase difference derivative was higher in the occipital-parietal region in the early stage of perturbations, before shifting to the parietal, motor, and back to the frontal-parietal channels. Continuous-time decoding of COP trajectories from EEG, using a gated recurrent unit model, achieved a mean Pearson's correlation coefficient of 0.7 ± 0.06. Overall, our findings suggest that EEG signals contain short-latency neural information related to an impending fall, which may be useful for developing brain-machine interface systems for fall prevention in robotic exoskeletons.},
}
@article {pmid35502788,
year = {2022},
author = {Wang, Y and Othayoth, R and Li, C},
title = {Cockroaches adjust body and appendages to traverse cluttered large obstacles.},
journal = {The Journal of experimental biology},
volume = {},
number = {},
pages = {},
doi = {10.1242/jeb.243605},
pmid = {35502788},
issn = {1477-9145},
support = {Beckman Young Investigator Award//Arnold and Mabel Beckman Foundation/ ; Career Award at the Scientific Interface//Burroughs Wellcome Fund/ ; start-up funds//Whiting School of Engineering, Johns Hopkins University/ ; undergraduate research internship//Tsinghua University/ ; },
abstract = {To traverse complex terrain, animals often transition between locomotor modes. It is well-known that locomotor transitions can be induced by switching in neural control circuits or driven by a need to minimize metabolic energetic cost. Recent work discovered that locomotor transitions in complex 3-D terrain cluttered with large obstacles can emerge from physical interaction with the environment controlled by the nervous system. For example, to traverse cluttered, stiff grass-like beams, the discoid cockroach often transitions from using a strenuous pitch mode pushing across to using a less strenuous roll mode rolling into and through the gaps, and this transition requires overcoming a potential energy barrier. Previous robotic physical modeling demonstrated that kinetic energy fluctuation of body oscillation from self-propulsion can help overcome the barrier and facilitate this transition. However, the animal was observed to transition even when the barrier still exceeded kinetic energy fluctuation. Here, we further studied whether and how the cockroach makes active adjustments to facilitate this transition to traverse cluttered beams. The animal repeatedly flexed its head and abdomen, reduced hind leg sprawl, and depressed one hind leg and elevated the other during the pitch-to-roll transition, which were absent when running on a flat ground. Using a refined potential energy landscape with additional degrees of freedom to model these adjustments, we found that head flexion did not substantially reduce the transition barrier, whereas leg sprawl reduction did so dramatically. We speculate that head flexion is for sensing the terrain to guide the transition via sensory feedback control.},
}
@article {pmid35500376,
year = {2022},
author = {Yuan, X and Zhang, L and Sun, Q and Lin, X and Li, C},
title = {A novel command generation method for SSVEP-based BCI by introducing SSVEP blocking response.},
journal = {Computers in biology and medicine},
volume = {146},
number = {},
pages = {105521},
doi = {10.1016/j.compbiomed.2022.105521},
pmid = {35500376},
issn = {1879-0534},
abstract = {Increasing the number of commands in a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) by increasing the number of visual stimuli has been widely studied. This paper proposes a novel BCI paradigm based on SSVEP and SSVEP blocking responses (defined as the disappearance or attenuation of the ongoing SSVEP) to increase the number of BCI commands with limited visual stimuli, in which the duration of SSVEP blocking response can be voluntarily controlled by users. Besides, this paper also proposes a frequency-specific threshold method and a unified threshold method to identify SSVEP blocking response. The paradigm includes a frequency recognition phase and an SSVEP blocking response identification phase. Filter bank canonical correlation analysis is used to detect the stimulation frequency, and the proposed threshold method is used to identify the SSVEP blocking response and calculate the blocking duration. The experimental results show that the two proposed threshold methods can effectively identify the SSVEP blocking response with different blocking duration and alternative stimulation frequencies. When there are Nf stimulation frequencies, the number of commands can be increased to Nf×Nt using the proposed paradigm, where Nt blocking durations correspond to each stimulus. This study demonstrates that the proposed paradigm based on SSVEP and SSVEP blocking responses is effective in increasing the number of BCI commands and has great potential for practical applications.},
}
@article {pmid35499947,
year = {2022},
author = {AlFarraj, A and AlIbrahim, M and AlHajjaj, H and Khater, F and AlGhamdi, A and Fayad, J},
title = {Transcutaneous Bone Conduction Implants in Patients With Single-Sided Deafness: Objective and Subjective Evaluation.},
journal = {Ear, nose, &amp; throat journal},
volume = {},
number = {},
pages = {1455613221099996},
doi = {10.1177/01455613221099996},
pmid = {35499947},
issn = {1942-7522},
abstract = {OBJECTIVES: This study aimed to investigate the audiological outcomes and subjective benefits of transcutaneous bone conduction implants (BCIs) in patients with single-sided deafness (SSD).<br><br>METHODS: This retrospective study was conducted on 11 patients with SSD implantations between 2015 and 2018 at a tertiary center. Pure-tone audiometry, speech reception threshold (SRT), and speech-in-noise (SPIN) tests were performed. Preoperative and postoperative performances were compared. Subjective satisfaction level was assessed using validated questionnaires. A PubMed search was conducted to identify the relevant studies published to date.<br><br>RESULTS: All patients demonstrated significant audiological improvements compared with their preoperative condition. The mean SRT improved significantly (p = 0.001) from 109 dB to 23 dB after implantation. The mean SPIN score improved significantly after implantation. The questionnaires showed an overall positive benefit of transcutaneous bone conduction devices (BCDs). A literature search revealed 21 articles, of which 14 reported the use of BCIs in patients with SSD. Our results agree with the published evidence showing the overall benefit of BCI in patients with SSD.<br><br>CONCLUSIONS: Transcutaneous BCDs could be considered as an alternative treatment option for patients with SSD, it could show good audiological outcomes and high satisfaction levels. Further studies should be conducted on patients with SSD to determine the most appropriate hearing solutions.},
}
@article {pmid35498243,
year = {2020},
author = {Mouli, S and Palaniappan, R},
title = {DIY hybrid SSVEP-P300 LED stimuli for BCI platform using EMOTIV EEG headset.},
journal = {HardwareX},
volume = {8},
number = {},
pages = {e00113},
doi = {10.1016/j.ohx.2020.e00113},
pmid = {35498243},
issn = {2468-0672},
abstract = {A fully customisable chip-on board (COB) LED design to evoke two brain responses simultaneously (steady state visual evoked potential (SSVEP) and transient evoked potential, P300) is discussed in this paper. Considering different possible modalities in brain-computer interfacing (BCI), SSVEP is widely accepted as it requires a lesser number of electroencephalogram (EEG) electrodes and minimal training time. The aim of this work was to produce a hybrid BCI hardware platform to evoke SSVEP and P300 precisely with reduced fatigue and improved classification performance. The system comprises of four independent radial green visual stimuli controlled individually by a 32-bit microcontroller platform to evoke SSVEP and four red LEDs flashing at random intervals to generate P300 events. The system can also record the P300 event timestamps that can be used in classification, to improve the accuracy and reliability. The hybrid stimulus was tested for real-time classification accuracy by controlling a LEGO robot to move in four directions.},
}
@article {pmid35496073,
year = {2022},
author = {Kostick-Quenet, K and Kalwani, L and Koenig, B and Torgerson, L and Sanchez, C and Munoz, K and Hsu, RL and Sierra-Mercado, D and Robinson, JO and Outram, S and Pereira, S and McGuire, A and Zuk, P and Lazaro-Munoz, G},
title = {Researchers' Ethical Concerns About Using Adaptive Deep Brain Stimulation for Enhancement.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {813922},
doi = {10.3389/fnhum.2022.813922},
pmid = {35496073},
issn = {1662-5161},
abstract = {The capacity of next-generation closed-loop or adaptive deep brain stimulation devices (aDBS) to read (measure neural activity) and write (stimulate brain regions or circuits) shows great potential to effectively manage movement, seizure, and psychiatric disorders, and also raises the possibility of using aDBS to electively (non-therapeutically) modulate mood, cognition, and prosociality. What separates aDBS from most neurotechnologies (e.g. transcranial stimulation) currently used for enhancement is that aDBS remains an invasive, surgically-implanted technology with a risk-benefit ratio significantly different when applied to diseased versus non-diseased individuals. Despite a large discourse about the ethics of enhancement, no empirical studies yet examine perspectives on enhancement from within the aDBS research community. We interviewed 23 aDBS researchers about their attitudes toward expanding aDBS use for enhancement. A thematic content analysis revealed that researchers share ethical concerns related to (1) safety and security; (2) enhancement as unnecessary, unnatural or aberrant; and (3) fairness, equality, and distributive justice. Most (70%) researchers felt that enhancement applications for DBS will eventually be technically feasible and that attempts to develop such applications for DBS are already happening (particularly for military purposes). However, researchers unanimously (100%) felt that DBS ideally should not be considered for enhancement until researchers better understand brain target localization and functioning. While many researchers acknowledged controversies highlighted by scholars and ethicists, such as potential impacts on personhood, authenticity, autonomy and privacy, their ethical concerns reflect considerations of both gravity and perceived near-term likelihood.},
}
@article {pmid35492533,
year = {2019},
author = {Wu, Y and Chen, H and Guo, L},
title = {Opportunities and dilemmas of in vitro nano neural electrodes.},
journal = {RSC advances},
volume = {10},
number = {1},
pages = {187-200},
doi = {10.1039/c9ra08917a},
pmid = {35492533},
issn = {2046-2069},
abstract = {Developing electrophysiological platforms to capture electrical activities of neurons and exert modulatory stimuli lays the foundation for many neuroscience-related disciplines, including the neuron-machine interface, neuroprosthesis, and mapping of brain circuitry. Intrinsically more advantageous than genetic and chemical neuronal probes, electrical interfaces directly target the fundamental driving force-transmembrane currents-behind the complicated and diverse neuronal signals, allowing for the discovery of neural computational mechanisms of the most accurate extent. Furthermore, establishing electrical access to neurons is so far the most promising solution to integrate large-scale, high-speed modern electronics with neurons that are highly dynamic and adaptive. Over the evolution of electrode-based electrophysiologies, there has long been a trade-off in terms of precision, invasiveness, and parallel access due to limitations in fabrication techniques and insufficient understanding of membrane-electrode interactions. On the one hand, intracellular platforms based on patch clamps and sharp electrodes suffer from acute cellular damage, fluid diffusion, and labor-intensive micromanipulation, with little room for parallel recordings. On the other hand, conventional extracellular microelectrode arrays cannot detect from subcellular compartments or capture subthreshold membrane potentials because of the large electrode size and poor seal resistance, making it impossible to depict a comprehensive picture of a neuron's electrical activities. Recently, the application of nanotechnology on neuronal electrophysiology has brought about a promising solution to mitigate these conflicts on a single chip. In particular, three dimensional nanostructures of 10-100 nm in diameter are naturally fit to achieve the purpose of precise and localized interrogations. Engineering them into vertical nanoprobes bound on planar substrates resulted in excellent membrane-electrode seals and high-density electrode distribution. There is no doubt that 3D vertical nanoelectrodes have achieved a fundamental milestone in terms of high precision, low invasiveness, and parallel recording at the neuron-electrode interface, albeit with there being substantial engineering issues that remain before the potential of nano neural interfaces can be fully exploited. Within this framework, we review the qualitative breakthroughs and opportunities brought about by 3D vertical nanoelectrodes, and discuss the major limitations of current electrode designs with respect to rational and seamless cell-on-chip systems.},
}
@article {pmid35492507,
year = {2022},
author = {King, JT and John, AR and Wang, YK and Shih, CK and Zhang, D and Huang, KC and Lin, CT},
title = {Brain Connectivity Changes During Bimanual and Rotated Motor Imagery.},
journal = {IEEE journal of translational engineering in health and medicine},
volume = {10},
number = {},
pages = {2100408},
doi = {10.1109/JTEHM.2022.3167552},
pmid = {35492507},
issn = {2168-2372},
abstract = {Motor imagery-based brain-computer interface (MI-BCI) currently represents a new trend in rehabilitation. However, individual differences in the responsive frequency bands and a poor understanding of the communication between the ipsilesional motor areas and other regions limit the use of MI-BCI therapy. Objective: Bimanual training has recently attracted attention as it achieves better outcomes as compared to repetitive one-handed training. This study compared the effects of three MI tasks with different visual feedback. Methods: Fourteen healthy subjects performed single hand motor imagery tasks while watching single static hand (traditional MI), single hand with rotation movement (rmMI), and bimanual coordination with a hand pedal exerciser (bcMI). Functional connectivity is estimated by Transfer Entropy (TE) analysis for brain information flow. Results: Brain connectivity of conducting three MI tasks showed that the bcMI demonstrated increased communications from the parietal to the bilateral prefrontal areas and increased contralateral connections between motor-related zones and spatial processing regions. Discussion/Conclusion: The results revealed bimanual coordination operation events increased spatial information and motor planning under the motor imagery task. And the proposed bimanual coordination MI-BCI (bcMI-BCI) can also achieve the effect of traditional motor imagery tasks and promotes more effective connections with different brain regions to better integrate motor-cortex functions for aiding the development of more effective MI-BCI therapy. Clinical and Translational Impact Statement The proposed bcMI-BCI provides more effective connections with different brain areas and integrates motor-cortex functions to promote motor imagery rehabilitation for patients' impairment.},
}
@article {pmid35490057,
year = {2022},
author = {Reinfeldt, S and Eeg-Olofsson, M and Fredén Jansson, KJ and Persson, AC and Håkansson, B},
title = {Long-term follow-up and review of the Bone Conduction Implant.},
journal = {Hearing research},
volume = {},
number = {},
pages = {108503},
doi = {10.1016/j.heares.2022.108503},
pmid = {35490057},
issn = {1878-5891},
abstract = {Active transcutaneous bone conduction devices are a type of bone conduction device developed to keep the skin intact and provide direct bone conduction stimulation. The Bone Conduction Implant (BCI) is such a device and has been implanted in 16 patients. The objective of this paper is to give a broad overview of the BCI development to the final results of 13 patients at 5-year follow-up. Follow-up of these patients included audiological performance investigations, questionnaires, as well as safety evaluation and objective functionality testing of the device. Among those audiological measurements were sound field warble tone thresholds, speech recognition threshold (SRT), speech recognition score (SRS) and signal to noise ratio threshold (SNR-threshold). The accumulated implant time for all 16 patients was 113 years in February 2022. During this time, no serious adverse events have occurred. The functional improvement for the 13 patients reported in this paper was on average 29.5 dB (average over 0.5, 1, 2 and 4 kHz), while the corresponding effective gain was -12.4 dB. The SRT improvement was 24.5 dB and the SRS improvement was 38.1%, while the aided SNR-threshold was on average -6.4 dB. It was found that the BCI can give effective and safe hearing rehabilitation for patients with conductive and mild-to-moderate mixed hearing loss.},
}
@article {pmid35488791,
year = {2022},
author = {Niazi, IK and Navid, MS and Rashid, U and Amjad, I and Olsen, S and Haavik, H and Alder, G and Kumari, N and Signal, N and Taylor, D and Farina, D and Jochumsen, M},
title = {Associative cued asynchronous BCI induces cortical plasticity in stroke patients.},
journal = {Annals of clinical and translational neurology},
volume = {},
number = {},
pages = {},
doi = {10.1002/acn3.51551},
pmid = {35488791},
issn = {2328-9503},
abstract = {OBJECTIVE: We propose a novel cue-based asynchronous brain-computer interface(BCI) for neuromodulation via the pairing of endogenous motor cortical activity with the activation of somatosensory pathways.<br><br>METHODS: The proposed BCI detects the intention to move from single-trial EEG signals in real time, but, contrary to classic asynchronous-BCI systems, the detection occurs only during time intervals when the patient is cued to move. This cue-based asynchronous-BCI was compared with two traditional BCI modes (asynchronous-BCI and offline synchronous-BCI) and a control intervention in chronic stroke patients. The patients performed ankle dorsiflexion movements of the paretic limb in each intervention while their brain signals were recorded. BCI interventions decoded the movement attempt and activated afferent pathways via electrical stimulation. Corticomotor excitability was assessed using motor-evoked potentials in the tibialis-anterior muscle induced by transcranial magnetic stimulation before, immediately after, and 30 min after the intervention.<br><br>RESULTS: The proposed cue-based asynchronous-BCI had significantly fewer false positives/min and false positives/true positives (%) as compared to the previously developed asynchronous-BCI. Linear-mixed-models showed that motor-evoked potential amplitudes increased following all BCI modes immediately after the intervention compared to the control condition (p <0.05). The proposed cue-based asynchronous-BCI resulted in the largest relative increase in peak-to-peak motor-evoked potential amplitudes(141% ± 33%) among all interventions and sustained it for 30 min(111% ± 33%).<br><br>INTERPRETATION: These findings prove the high performance of a newly proposed cue-based asynchronous-BCI intervention. In this paradigm, individuals receive precise instructions (cue) to promote engagement, while the timing of brain activity is accurately detected to establish a precise association with the delivery of sensory input for plasticity induction.},
}
@article {pmid35483505,
year = {2022},
author = {Ouyang, R and Jin, Z and Tang, S and Fan, C and Wu, X},
title = {Low-quality Training Data Detection Method of EEG Signals for Motor Imagery BCI System.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {109607},
doi = {10.1016/j.jneumeth.2022.109607},
pmid = {35483505},
issn = {1872-678X},
abstract = {BACKGROUND: The design and implementation of high-performance motor imagery-based brain computer interface (MI-BCI) requires high-quality training samples. However, fluctuation in subjects' physiological and mental states as well as artifacts can produce the low-quality motor imagery electroencephalogram (EEG) signal, which will damage the performance of MI-BCI system.<br><br>NEW METHOD: In order to select high-quality MI-EEG training data, this paper proposes a low-quality training data detection method combining independent component analysis (ICA) and weak classifier cluster. we also design and implement a new online BCI system based on motor imagery to verify the online processing performance of the proposed method.<br><br>RESULT: In order to verify the effectiveness of the proposed method, we conducted offline experiments on the public dataset called BCI Competition IV Data Set 2b. Furthermore, in order to verify the processing performance of the online system, we designed 60 groups of online experiments on 12 subjects. The online experimental results show that the twelve subjects can complete the system task efficiently (the best experiment is 135.6 seconds with 9 trials of subject S1).<br><br>CONCLUSION: This paper demonstrated that the proposed low-quality training data detection method can effectively screen out low-quality training samples, so as to improve the performance of the MI-BCI system.},
}
@article {pmid35483331,
year = {2022},
author = {Yan, W and Wu, Y and Du, C and Xu, G},
title = {Cross-subject Spatial Filter Transfer Method for SSVEP-EEG Feature Recognition.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ac6b57},
pmid = {35483331},
issn = {1741-2552},
abstract = {OBJECTIVE: Steady-state visual evoked potential (SSVEP) is an important control method of the brain-computer interface (BCI) system. The development of an efficient SSVEP feature decoding algorithm is the core issue in SSVEP-BCI. It has been proposed to use user training data to reduce the spontaneous electroencephalogram (EEG) activity interference on SSVEP response, thereby improving the feature recognition accuracy of the SSVEP signal. Nevertheless, the tedious data collection process increases the mental fatigue of the user and severely affects the applicability of the BCI system.<br><br>APPROACH: A cross-subject spatial filter transfer (CSSFT) method that transfer the existing user data model to the new user test data without collecting any training data from the new user is proposed.<br><br>MAIN RESULTS: Experimental results demonstrate that the transfer model increases the distinction of the feature discriminant coefficient between the gaze following target and the non-gaze following target and accurately identifies the wrong target in the fundamental algorithm model. The public datasets show that the CSSFT method significantly increases the recognition performance of canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA). Additionally, when the data used to calculate the transfer model contains one data block only, the CSSFT method retains its effective feature recognition capabilities.<br><br>SIGNIFICANCE: The proposed method requires no tedious data calibration process, provides an effective technical solution for the transfer of the cross-subject model, and has potential application value for promoting the application of the BCI system.},
}
@article {pmid35483227,
year = {2022},
author = {Aurna, NF and Yousuf, MA and Taher, KA and Azad, AKM and Moni, MA},
title = {A classification of MRI brain tumor based on two stage feature level ensemble of deep CNN models.},
journal = {Computers in biology and medicine},
volume = {146},
number = {},
pages = {105539},
doi = {10.1016/j.compbiomed.2022.105539},
pmid = {35483227},
issn = {1879-0534},
abstract = {The brain tumor is one of the deadliest cancerous diseases and its severity has turned it to the leading cause of cancer related mortality. The treatment procedure of the brain tumor depends on the type, location and size of the tumor. Relying solely on human inspection for precise categorization can lead to inevitably dangerous situation. This manual diagnosis process can be improved and accelerated through an automated Computer Aided Diagnosis (CADx) system. In this article, a novel approach using two-stage feature ensemble of deep Convolutional Neural Networks (CNN) is proposed for precise and automatic classification of brain tumors. Three unique Magnetic Resonance Imaging (MRI) datasets and a dataset merging all the unique datasets are considered. The datasets contain three types of brain tumor (meningioma, glioma, pituitary) and normal brain images. From five pre-trained models and a proposed CNN model, the best models are chosen and concatenated in two stages for feature extraction. The best classifier is also chosen among five different classifiers based on accuracy. From the extracted features, most substantial features are selected using Principal Component Analysis (PCA) and fed into the classifier. The robustness of the proposed two stage ensemble model is analyzed using several performance metrics and three different experiments. Through the prominent performance, the proposed model is able to outperform other existing models attaining an average accuracy of 99.13% by optimization of the developed algorithms. Here, the individual accuracy for Dataset 1, Dataset 2, Dataset 3, and Merged Dataset is 99.67%, 98.16%, 99.76%, and 98.96% respectively. Finally a User Interface (UI) is created using the proposed model for real time validation.},
}
@article {pmid35482705,
year = {2022},
author = {Maÿe, A and Rauterberg, R and Engel, AK},
title = {Instant classification for the spatially-coded BCI.},
journal = {PloS one},
volume = {17},
number = {4},
pages = {e0267548},
doi = {10.1371/journal.pone.0267548},
pmid = {35482705},
issn = {1932-6203},
abstract = {The spatially-coded SSVEP BCI exploits changes in the topography of the steady-state visual evoked response to visual flicker stimulation in the extrafoveal field of view. In contrast to frequency-coded SSVEP BCIs, the operator does not gaze into any flickering lights; therefore, this paradigm can reduce visual fatigue. Other advantages include high classification accuracies and a simplified stimulation setup. Previous studies of the paradigm used stimulation intervals of a fixed duration. For frequency-coded SSVEP BCIs, it has been shown that dynamically adjusting the trial duration can increase the system's information transfer rate (ITR). We therefore investigated whether a similar increase could be achieved for spatially-coded BCIs by applying dynamic stopping methods. To this end we introduced a new stopping criterion which combines the likelihood of the classification result and its stability across larger data windows. Whereas the BCI achieved an average ITR of 28.4±6.4 bits/min with fixed intervals, dynamic intervals increased the performance to 81.1±44.4 bits/min. Users were able to maintain performance up to 60 minutes of continuous operation. We suggest that the dynamic response time might have worked as a kind of temporal feedback which allowed operators to optimize their brain signals and compensate fatigue.},
}
@article {pmid35480157,
year = {2022},
author = {Cai, X and Pan, J},
title = {Toward a Brain-Computer Interface- and Internet of Things-Based Smart Ward Collaborative System Using Hybrid Signals.},
journal = {Journal of healthcare engineering},
volume = {2022},
number = {},
pages = {6894392},
doi = {10.1155/2022/6894392},
pmid = {35480157},
issn = {2040-2309},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Electrooculography/methods ; Humans ; *Internet of Things ; Movement ; },
abstract = {This study proposes a brain-computer interface (BCI)- and Internet of Things (IoT)-based smart ward collaborative system using hybrid signals. The system is divided into hybrid asynchronous electroencephalography (EEG)-, electrooculography (EOG)- and gyro-based BCI control system and an IoT monitoring and management system. The hybrid BCI control system proposes a GUI paradigm with cursor movement. The user uses the gyro to control the cursor area selection and uses blink-related EOG to control the cursor click. Meanwhile, the attention-related EEG signals are classified based on a support-vector machine (SVM) to make the final judgment. The judgment of the cursor area and the judgment of the attention state are reduced, thereby reducing the false operation rate in the hybrid BCI system. The accuracy in the hybrid BCI control system was 96.65 ± 1.44%, and the false operation rate and command response time were 0.89 ± 0.42 events/min and 2.65 ± 0.48 s, respectively. These results show the application potential of the hybrid BCI control system in daily tasks. In addition, we develop an architecture to connect intelligent things in a smart ward based on narrowband Internet of Things (NB-IoT) technology. The results demonstrate that our system provides superior communication transmission quality.},
}
@article {pmid35478847,
year = {2022},
author = {Chen, Z and Ye, N and Teng, C and Li, X},
title = {Alternations and Applications of the Structural and Functional Connectome in Gliomas: A Mini-Review.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {856808},
doi = {10.3389/fnins.2022.856808},
pmid = {35478847},
issn = {1662-4548},
abstract = {In the central nervous system, gliomas are the most common, but complex primary tumors. Genome-based molecular and clinical studies have revealed different classifications and subtypes of gliomas. Neuroradiological approaches have non-invasively provided a macroscopic view for surgical resection and therapeutic effects. The connectome is a structural map of a physical object, the brain, which raises issues of spatial scale and definition, and it is calculated through diffusion magnetic resonance imaging (MRI) and functional MRI. In this study, we reviewed the basic principles and attributes of the structural and functional connectome, followed by the alternations of connectomes and their influences on glioma. To extend the applications of connectome, we demonstrated that a series of multi-center projects still need to be conducted to systemically investigate the connectome and the structural-functional coupling of glioma. Additionally, the brain-computer interface based on accurate connectome could provide more precise structural and functional data, which are significant for surgery and postoperative recovery. Besides, integrating the data from different sources, including connectome and other omics information, and their processing with artificial intelligence, together with validated biological and clinical findings will be significant for the development of a personalized surgical strategy.},
}
@article {pmid35478300,
year = {2022},
author = {Thompson, EM and Patel, V and Rajeeve, V and Cutillas, PR and Stoker, AW},
title = {The cytotoxic action of BCI is not dependent on its stated DUSP1 or DUSP6 targets in neuroblastoma cells.},
journal = {FEBS open bio},
volume = {},
number = {},
pages = {},
doi = {10.1002/2211-5463.13418},
pmid = {35478300},
issn = {2211-5463},
abstract = {Neuroblastoma (NB) is a heterogenous cancer of the sympathetic nervous system which accounts for 7-10% of paediatric malignancies worldwide. Due to the lack of targetable molecular aberrations in NB, most treatment options remain relatively non-specific. Here, we investigated the therapeutic potential of BCI, an inhibitor of DUSP1 and DUSP6, in cultured NB cells. BCI was cytotoxic in a range of NB cell lines and induced a short-lived activation of the AKT and stress-inducible MAP kinases, although ERK phosphorylation was unaffected. Furthermore, a phosphoproteomic screen identified significant upregulation of JNK signalling components and a suppression in mTOR and R6K signalling. To assess the specificity of BCI, CRISPR-Cas9 was employed to introduce insertions and deletions in the DUSP1 and DUSP6 genes. Surprisingly, BCI remained fully cytotoxic in NB cells with complete loss of DUSP6 and partial depletion of DUSP1, suggesting that BCI exerts cytotoxicity in NB cells through a complex mechanism that is unrelated to these phosphatases. Overall, these data highlight the risk of using inhibitors such as BCI as a supposedly specific DUSP1/6 inhibitor, without understanding its full range of targets in cancer cells.},
}
@article {pmid35477130,
year = {2022},
author = {Zhang, R and Xu, Z and Zhang, L and Cao, L and Hu, Y and Lu, B and Shi, L and Yao, D and Zhao, X},
title = {The effect of stimulus number on the recognition accuracy and information transfer rate of SSVEP-BCI in augmented reality.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ac6ae5},
pmid = {35477130},
issn = {1741-2552},
abstract = {OBJECTIVE: The biggest advantage of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) lies in its large command set and high information transfer rate (ITR). Almost all current SSVEP-BCIs use a computer screen (CS) to present flickering visual stimuli, which limits its flexible use in actual scenes. Augmented reality (AR) technology provides the ability to superimpose visual stimuli on the real world, and it considerably expands the application scenarios of SSVEP-BCI. However, whether the advantages of SSVEP-BCI can be maintained when moving the visual stimuli to AR glasses is not known. This study investigated the effects of the stimulus number for SSVEP-BCI in an AR context.<br><br>APPROACH: We designed SSVEP flickering stimulation interfaces with four different numbers of stimulus targets and put them in AR glasses and a CS to display. Three common recognition algorithms were used to analyze the influence of the stimulus number and stimulation time on the recognition accuracy and ITR of AR-SSVEP and CS-SSVEP.<br><br>MAIN RESULTS: The amplitude spectrum and signal-to-noise ratio of AR-SSVEP were not significantly different from CS-SSVEP at the fundamental frequency but were significantly lower than CS-SSVEP at the second harmonic. SSVEP recognition accuracy decreased as the stimulus number increased in AR-SSVEP but not in CS-SSVEP. When the stimulus number increased, the maximum ITR of CS-SSVEP also increased, but not for AR-SSVEP. When the stimulus number was 25, the maximum ITR (142.05 bits/min) was reached at 400 ms. The importance of stimulation time in SSVEP was confirmed. When the stimulation time became longer, the recognition accuracy of both AR-SSVEP and CS-SSVEP increased. The peak value was reached at 3 s. The ITR increased first and then slowly decreased after reaching the peak value.<br><br>SIGNIFICANCE: Our study indicates that the conclusions based on CS-SSVEP cannot be simply applied to AR-SSVEP, and it is not advisable to set too many stimulus targets in the AR display device.},
}
@article {pmid35475424,
year = {2022},
author = {van Velthoven, EAM and van Stuijvenberg, OC and Haselager, DRE and Broekman, M and Chen, X and Roelfsema, P and Bredenoord, AL and Jongsma, KR},
title = {Ethical implications of visual neuroprostheses-a systematic review.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac65b2},
pmid = {35475424},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Neural Prostheses ; },
abstract = {Objective. The aim of this review was to systematically identify the ethical implications of visual neuroprostheses.Approach. A systematic search was performed in both PubMed and Embase using a search string that combined synonyms for visual neuroprostheses, brain-computer interfaces (BCIs), cochlear implants (CIs), and ethics. We chose to include literature on BCIs and CIs, because of their ethically relavant similarities and functional parallels with visual neuroprostheses.Main results. We included 84 articles in total. Six focused specifically on visual prostheses. The other articles focused more broadly on neurotechnologies, on BCIs or CIs. We identified 169 ethical implications that have been categorized under seven main themes: (a) benefits for health and well-being; (b) harm and risk; (c) autonomy; (d) societal effects; (e) clinical research; (f) regulation and governance; and (g) involvement of experts, patients and the public.Significance. The development and clinical use of visual neuroprostheses is accompanied by ethical issues that should be considered early in the technological development process. Though there is ample literature on the ethical implications of other types of neuroprostheses, such as motor neuroprostheses and CIs, there is a significant gap in the literature regarding the ethical implications of visual neuroprostheses. Our findings can serve as a starting point for further research and normative analysis.},
}
@article {pmid35475302,
year = {2021},
author = {Jaipuria, J and Karimi, AM and Singh, A and Thapa, BB and Gupta, S and Sadasukhi, N and Venkatasubramaniyan, M and Pathak, P and Kasaraneni, P and Khanna, A and Narayan, TA and Sharma, G and Rawal, S},
title = {Pitcher pot neourethral modification of ileal orthotopic neobladder achieves satisfactory long-term functional and quality of life outcomes with low clean intermittent self-catheterization rate.},
journal = {BJUI compass},
volume = {2},
number = {4},
pages = {292-299},
pmid = {35475302},
issn = {2688-4526},
abstract = {Objective: To describe a decade of our experience with a neo-urethral modification of ileal orthotopic neobladder (pitcher pot ONB). Multiple investigators have reported similar modifications. However, long-term longitudinal functional and quality of life (QOL) outcomes are lacking.<br><br>Methods: Prospectively maintained hospital registry for 238 ONB patients comprising a mix of open and robotic surgery cohorts from 2007 to 2017, and minimum of 2 years of follow-up was retrospectively queried. QOL was evaluated using Bladder Cancer Index (BCI). Longitudinal trends of QOL domain parameters were analysed. List of perioperative variables that have a biologically plausible association with continence, potency, and post-operative BCI QOL sexual, urinary, and bowel domain scores was drawn. Variables included surgery type, Body Mass Index (BMI), T and N stage, neurovascular bundle (NVB) sparing, age, and related pre-operative BCI QOL domain score. Prognostic associations were analysed using multivariable Cox proportional hazard models and multilevel mixed-effects modeling.<br><br>Results: The study comprised 80 and 158 patients who underwent open and robotic sandwich technique cohorts, respectively. Open surgery was associated with significantly higher "any" complication (40% vs 27%, P-value .050) and "major" complication rate (15% vs 11%, P-value .048). All patients developed a bladder capacity >400 cc with negligible post-void residual urine, and all but one patient achieved spontaneous voiding by the end of study period (<1% clean intermittent self-catheterization [CISC] rate). By 15 months, QOL for all three domains had recovered to reach a plateau. About 45% of patients achieved potency, and the median time to achieve day and night time continence was 9 and 12 months respectively. Lower age and NVBs spared during surgery were found to be significantly associated with the earlier achievement of potency, day and night time continence, as well as better urinary and sexual summary QOL scores.<br><br>Conclusions: Pitcher pot neobladder achieves satisfactory long-term functional and QOL outcomes with negligible CISC rate. Results were superior with incremental nerves spared during surgery.},
}
@article {pmid35473959,
year = {2022},
author = {Jalilpour, S and Müller-Putz, G},
title = {Toward passive BCI: asynchronous decoding of neural responses to direction- and angle-specific perturbations during a simulated cockpit scenario.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {6802},
pmid = {35473959},
issn = {2045-2322},
mesh = {*Automobile Driving ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials/physiology ; Humans ; },
abstract = {Neuroimaging studies have provided proof that loss of balance evokes specific neural transient wave complexes in electroencephalography (EEG), called perturbation evoked potentials (PEPs). Online decoding of balance perturbations from ongoing EEG signals can establish the possibility of implementing passive brain-computer interfaces (pBCIs) as a part of aviation/driving assistant systems. In this study, we investigated the feasibility of identifying the existence and expression of perturbations in four different conditions by using EEG signals. Fifteen healthy participants experienced four various postural changes while they sat in a glider cockpit. Sudden perturbations were exposed by a robot connected to a glider and moved to the right and left directions with tilting angles of 5 and 10 degrees. Perturbations occurred in an oddball paradigm in which participants were not aware of the time and expression of the perturbations. We employed a hierarchical approach to separate the perturbation and rest, and then discriminate the expression of perturbations. The performance of the BCI system was evaluated by using classification accuracy and F1 score. Asynchronously, we achieved average accuracies of 89.83 and 73.64% and average F1 scores of 0.93 and 0.60 for binary and multiclass classification, respectively. These results manifest the practicality of pBCI for the detection of balance disturbances in a realistic situation.},
}
@article {pmid35467033,
year = {2022},
author = {Han, JJ},
title = {A man in a completely locked-in state produces intelligible sentences using a brain-computer interface.},
journal = {Artificial organs},
volume = {},
number = {},
pages = {},
doi = {10.1111/aor.14249},
pmid = {35467033},
issn = {1525-1594},
abstract = {Patients with amyotrophic lateral sclerosis may enter into a completely locked-in state without any capability for communication using neuromuscular output. Using an auditory-guided neurofeedback-based strategy with implantable sensors in the motor cortex, scientists were able to help a patient in this state produce intelligible sentences.},
}
@article {pmid35465540,
year = {2022},
author = {Wang, Y and Yang, Z and Ji, H and Li, J and Liu, L and Zhuang, J},
title = {Cross-Modal Transfer Learning From EEG to Functional Near-Infrared Spectroscopy for Classification Task in Brain-Computer Interface System.},
journal = {Frontiers in psychology},
volume = {13},
number = {},
pages = {833007},
pmid = {35465540},
issn = {1664-1078},
abstract = {The brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) has received more and more attention due to its vast application potential in emotion recognition. However, the relatively insufficient investigation of the feature extraction algorithms limits its use in practice. In this article, to improve the performance of fNIRS-based BCI, we proposed a method named R-CSP-E, which introduces EEG signals when computing fNIRS signals' features based on transfer learning and ensemble learning theory. In detail, we used the Independent Component Analysis (ICA) algorithm for the correspondence between the sources of the two signals. We then introduced the EEG signals when computing the spatial filter based on a modified Common Spatial Pattern (CSP) algorithm. Experimental results on public datasets show that the proposed method in this paper outperforms traditional methods without transfer. In general, the mean classification accuracy can be increased by up to 5%. To our knowledge, it is an innovation that we tried to apply transfer learning between EEG and fNIRS. Our study's findings not only prove the potential of the transfer learning algorithm in cross-model brain-computer interface, but also offer a new and innovative perspective to research the hybrid brain-computer interface.},
}
@article {pmid35464315,
year = {2022},
author = {Liu, L and Jin, M and Zhang, L and Zhang, Q and Hu, D and Jin, L and Nie, Z},
title = {Brain-Computer Interface-Robot Training Enhances Upper Extremity Performance and Changes the Cortical Activation in Stroke Patients: A Functional Near-Infrared Spectroscopy Study.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {809657},
pmid = {35464315},
issn = {1662-4548},
abstract = {Introduction: We evaluated the efficacy of brain-computer interface (BCI) training to explore the hypothesized beneficial effects of physiotherapy alone in chronic stroke patients with moderate or severe paresis. We also focused on the neuroplastic changes in the primary motor cortex (M1) after BCI training.<br><br>Methods: In this study, 18 hospitalized chronic stroke patients with moderate or severe motor deficits participated. Patients were operated on for 20 sessions and followed up after 1 month. Functional assessments were performed at five points, namely, pre1-, pre2-, mid-, post-training, and 1-month follow-up. Wolf Motor Function Test (WMFT) was used as the primary outcome measure, while Fugl-Meyer Assessment (FMA), its wrist and hand (FMA-WH) sub-score and its shoulder and elbow (FMA-SE) sub-score served as secondary outcome measures. Neuroplastic changes were measured by functional near-infrared spectroscopy (fNIRS) at baseline and after 20 sessions of BCI training. Pearson correlation analysis was used to evaluate functional connectivity (FC) across time points.<br><br>Results: Compared to the baseline, better functional outcome was observed after BCI training and 1-month follow-up, including a significantly higher probability of achieving a clinically relevant increase in the WMFT full score (ΔWMFT score = 12.39 points, F = 30.28, and P < 0.001), WMFT completion time (ΔWMFT time = 248.39 s, F = 16.83, and P < 0.001), and FMA full score (ΔFMA-UE = 12.72 points, F = 106.07, and P < 0.001), FMA-WH sub-score (ΔFMA-WH = 5.6 points, F = 35.53, and P < 0.001), and FMA-SE sub-score (ΔFMA-SE = 8.06 points, F = 22.38, and P < 0.001). Compared to the baseline, after BCI training the FC between the ipsilateral M1 and the contralateral M1 was increased (P < 0.05), which was the same as the FC between the ipsilateral M1 and the ipsilateral frontal lobe, and the FC between the contralateral M1 and the contralateral frontal lobe was also increased (P < 0.05).<br><br>Conclusion: The findings demonstrate that BCI-based rehabilitation could be an effective intervention for the motor performance of patients after stroke with moderate or severe upper limb paresis and represents a potential strategy in stroke neurorehabilitation. Our results suggest that FC between ipsilesional M1 and frontal cortex might be enhanced after BCI training.<br><br>Clinical Trial Registration: www.chictr.org.cn, identifier: ChiCTR2100046301.},
}
@article {pmid35463935,
year = {2022},
author = {Zhou, Q and Cheng, R and Yao, L and Ye, X and Xu, K},
title = {Neurofeedback Training of Alpha Relative Power Improves the Performance of Motor Imagery Brain-Computer Interface.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {831995},
pmid = {35463935},
issn = {1662-5161},
abstract = {Significant variation in performance in motor imagery (MI) tasks impedes their wide adoption for brain-computer interface (BCI) applications. Previous researchers have found that resting-state alpha-band power is positively correlated with MI-BCI performance. In this study, we designed a neurofeedback training (NFT) protocol based on the up-regulation of the alpha band relative power (RP) to investigate its effect on MI-BCI performance. The principal finding of this study is that alpha NFT could successfully help subjects increase alpha-rhythm power and improve their MI-BCI performance. An individual difference was also found in this study in that subjects who increased alpha power more had a better performance improvement. Additionally, the functional connectivity (FC) of the frontal-parietal (FP) network was found to be enhanced after alpha NFT. However, the enhancement failed to reach a significant level after multiple comparisons correction. These findings contribute to a better understanding of the neurophysiological mechanism of cognitive control through alpha regulation.},
}
@article {pmid35463924,
year = {2022},
author = {Belkacem, AN and Falk, TH and Yanagisawa, T and Guger, C},
title = {Editorial: Cognitive and Motor Control Based on Brain-Computer Interfaces for Improving the Health and Well-Being in Older Age.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {881922},
doi = {10.3389/fnhum.2022.881922},
pmid = {35463924},
issn = {1662-5161},
}
@article {pmid35463262,
year = {2022},
author = {Pan, J and Yang, F and Qiu, L and Huang, H},
title = {Fusion of EEG-Based Activation, Spatial, and Connection Patterns for Fear Emotion Recognition.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {3854513},
pmid = {35463262},
issn = {1687-5273},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Emotions/physiology ; Fear ; Humans ; },
abstract = {At present, emotion recognition based on electroencephalograms (EEGs) has attracted much more attention. Current studies of affective brain-computer interfaces (BCIs) focus on the recognition of happiness and sadness using brain activation patterns. Fear recognition involving brain activities in different spatial distributions and different brain functional networks has been scarcely investigated. In this study, we propose a multifeature fusion method combining energy activation, spatial distribution, and brain functional connection network (BFCN) features for fear emotion recognition. The affective brain pattern was identified by not only the power activation features of differential entropy (DE) but also the spatial distribution features of the common spatial pattern (CSP) and the EEG phase synchronization features of phase lock value (PLV). A total of 15 healthy subjects took part in the experiment, and the average accuracy rate was 85.00% ± 8.13%. The experimental results showed that the fear emotions of subjects were fully stimulated and effectively identified. The proposed fusion method on fear recognition was thus validated and is of great significance to the development of effective emotional BCI systems.},
}
@article {pmid35463256,
year = {2022},
author = {Huang, Z and Cheng, L and Liu, Y},
title = {Key Feature Extraction Method of Electroencephalogram Signal by Independent Component Analysis for Athlete Selection and Training.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {6752067},
pmid = {35463256},
issn = {1687-5273},
mesh = {Algorithms ; *Artificial Intelligence ; Athletes ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {Emotion is an important expression generated by human beings to external stimuli in the process of interaction with the external environment. It affects all aspects of our lives all the time. Accurate identification of human emotional states and further application in artificial intelligence can better improve and assist human life. Therefore, the research on emotion recognition has attracted the attention of many scholars in the field of artificial intelligence in recent years. Brain electrical signal conversion becomes critical, and it needs a brain electrical signal processing method to extract the effective signal to realize the human-computer interaction However, nonstationary nonlinear characteristics of EEG signals bring great challenge in characteristic signal extraction. At present, although there are many feature extraction methods, none of them can reflect the global feature of the signal. The following solutions are used to solve the above problems: (1) this paper proposed an ICA and sample entropy algorithm-based framework for feature extraction of EEG signals, which has not been applied for EEG and (2) simulation signals were used to verify the feasibility of this method, and experiments were carried out on two real-world data sets, to show the advantages of the new algorithm in feature extraction of EEG signals.},
}
@article {pmid35462690,
year = {2022},
author = {Li, L and Zhang, Y and Huang, L and Zhao, J and Wang, J and Liu, T},
title = {Robot Assisted Treatment of Hand Functional Rehabilitation Based on Visual Motor Imagination.},
journal = {Frontiers in aging neuroscience},
volume = {14},
number = {},
pages = {870871},
pmid = {35462690},
issn = {1663-4365},
abstract = {This pilot study implements a hybrid brain computer interface paradigm based on motor imagery (MI) and steady-state visual evoked potential (SSVEP), in order to explore the neural mechanism and clinical effect of MI-SSVEP intervention paradigm on upper limb functional rehabilitation. In this study, EEG data of 12 healthy participants were collected, and the activation regions of MI-SSVEP paradigm were identified by power spectral density (PSD). By analyzing the inter trial phase consistency (ITPC) of characteristic regions and the causal relationship of brain network, the motor cognitive process including high-level somatosensory joint cortex in the intervention process of MI-SSVEP was studied. Subsequently, this study verified the clinical effect of MI-SSVEP intervention paradigm for 61 stroke patients. The results show that the robot assisted therapy using MI-SSVEP intervention paradigm can more effectively improve the rehabilitation effect of patients.},
}
@article {pmid35462356,
year = {2022},
author = {Gehrke, L and Lopes, P and Klug, M and Akman, S and Gramann, K},
title = {Neural Sources of Prediction Errors Detect Unrealistic VR Interactions.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ac69bc},
pmid = {35462356},
issn = {1741-2552},
abstract = {Objective Neural interfaces hold significant promise to implicitly track user experience. Their application in VR/AR simulations is especially favorable as it allows user assessment without breaking the immersive experience. In VR, designing immersion is one key challenge. Subjective questionnaires are the established metrics to assess the effectiveness of immersive VR simulations. However, administering such questionnaires requires breaking the immersive experience they are supposed to assess. Approach We present a complimentary metric based on a ERPs. For the metric to be robust, the neural signal employed must be reliable. Hence, it is beneficial to target the neural signal's cortical origin directly, efficiently separating signal from noise. To test this new complementary metric, we designed a reach-to-tap paradigm in VR to probe EEG and movement adaptation to visuo-haptic glitches. Our working hypothesis was, that these glitches, or violations of the predicted action outcome, may indicate a disrupted user experience. Main Results Using prediction error negativity features, we classified VR glitches with ~77\% accuracy. We localized the EEG sources driving the classification and found midline cingulate EEG sources and a distributed network of parieto-occipital EEG sources to enable the classification success. Significance Prediction error signatures from these sources reflect violations of user's predictions during interaction with AR/VR, promising a robust and targeted marker for adaptive user interfaces.},
}
@article {pmid35459070,
year = {2022},
author = {Butt, AM and Alsaffar, H and Alshareef, M and Qureshi, KK},
title = {AI Prediction of Brain Signals for Human Gait Using BCI Device and FBG Based Sensorial Platform for Plantar Pressure Measurements.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {8},
pages = {},
pmid = {35459070},
issn = {1424-8220},
support = {SR 191027//Deanship of Research, Oversight, and Coordination, KFUPM/ ; },
mesh = {Artificial Intelligence ; Bayes Theorem ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Gait ; Humans ; },
abstract = {Artificial intelligence (AI) in developing modern solutions for biomedical problems such as the prediction of human gait for human rehabilitation is gaining ground. An attempt was made to use plantar pressure information through fiber Bragg grating (FBG) sensors mounted on an in-sole, in tandem with a brain-computer interface (BCI) device to predict brain signals corresponding to sitting, standing and walking postures of a person. Posture classification was attained with an accuracy range between 87-93% from FBG and BCI signals using machine learning models such as K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and naïve Bayes (NB). These models were used to identify electrodes responding to sitting, standing and walking activities of four users from a 16 channel BCI device. Six electrode positions based on the 10-20 system for electroencephalography (EEG) were identified as the most sensitive to plantar activities and found to be consistent with clinical investigations of the sensorimotor cortex during foot movement. A prediction of brain EEG corresponding to given FBG data with lowest mean square error (MSE) values (0.065-0.109) was made with the selection of a long-short term memory (LSTM) machine learning model when compared to the recurrent neural network (RNN) and gated recurrent unit (GRU) models.},
}
@article {pmid35458962,
year = {2022},
author = {Algarni, M and Saeed, F and Al-Hadhrami, T and Ghabban, F and Al-Sarem, M},
title = {Deep Learning-Based Approach for Emotion Recognition Using Electroencephalography (EEG) Signals Using Bi-Directional Long Short-Term Memory (Bi-LSTM).},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {8},
pages = {},
pmid = {35458962},
issn = {1424-8220},
support = {77 /442//The Deputyship for Research &amp; Innovation, Ministry of Education in Saudi Arabia/ ; },
mesh = {*Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Emotions ; Memory, Short-Term ; },
abstract = {Emotions are an essential part of daily human communication. The emotional states and dynamics of the brain can be linked by electroencephalography (EEG) signals that can be used by the Brain-Computer Interface (BCI), to provide better human-machine interactions. Several studies have been conducted in the field of emotion recognition. However, one of the most important issues facing the emotion recognition process, using EEG signals, is the accuracy of recognition. This paper proposes a deep learning-based approach for emotion recognition through EEG signals, which includes data selection, feature extraction, feature selection and classification phases. This research serves the medical field, as the emotion recognition model helps diagnose psychological and behavioral disorders. The research contributes to improving the performance of the emotion recognition model to obtain more accurate results, which, in turn, aids in making the correct medical decisions. A standard pre-processed Database of Emotion Analysis using Physiological signaling (DEAP) was used in this work. The statistical features, wavelet features, and Hurst exponent were extracted from the dataset. The feature selection task was implemented through the Binary Gray Wolf Optimizer. At the classification stage, the stacked bi-directional Long Short-Term Memory (Bi-LSTM) Model was used to recognize human emotions. In this paper, emotions are classified into three main classes: arousal, valence and liking. The proposed approach achieved high accuracy compared to the methods used in past studies, with an average accuracy of 99.45%, 96.87% and 99.68% of valence, arousal, and liking, respectively, which is considered a high performance for the emotion recognition model.},
}
@article {pmid35458940,
year = {2022},
author = {Phadikar, S and Sinha, N and Ghosh, R and Ghaderpour, E},
title = {Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-Heuristically Optimized Non-Local Means Filter.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {8},
pages = {},
pmid = {35458940},
issn = {1424-8220},
support = {1-5770264050//Ministry of Human Resource Development/ ; },
mesh = {Algorithms ; *Artifacts ; Electroencephalography/methods ; Muscles ; Signal Processing, Computer-Assisted ; *Wavelet Analysis ; },
abstract = {Electroencephalogram (EEG) signals may get easily contaminated by muscle artifacts, which may lead to wrong interpretation in the brain-computer interface (BCI) system as well as in various medical diagnoses. The main objective of this paper is to remove muscle artifacts without distorting the information contained in the EEG. A novel multi-stage EEG denoising method is proposed for the first time in which wavelet packet decomposition (WPD) is combined with a modified non-local means (NLM) algorithm. At first, the artifact EEG signal is identified through a pre-trained classifier. Next, the identified EEG signal is decomposed into wavelet coefficients and corrected through a modified NLM filter. Finally, the artifact-free EEG is reconstructed from corrected wavelet coefficients through inverse WPD. To optimize the filter parameters, two meta-heuristic algorithms are used in this paper for the first time. The proposed system is first validated on simulated EEG data and then tested on real EEG data. The proposed approach achieved average mutual information (MI) as 2.9684 ± 0.7045 on real EEG data. The result reveals that the proposed system outperforms recently developed denoising techniques with higher average MI, which indicates that the proposed approach is better in terms of quality of reconstruction and is fully automatic.},
}
@article {pmid35458297,
year = {2022},
author = {Afzali, M and Boateng, JS},
title = {Composite Fish Collagen-Hyaluronate Based Lyophilized Scaffolds Modified with Sodium Alginate for Potential Treatment of Chronic Wounds.},
journal = {Polymers},
volume = {14},
number = {8},
pages = {},
pmid = {35458297},
issn = {2073-4360},
abstract = {Chronic wounds are characterized by both decreased collagen deposition and increased collagen breakdown. It is reasonable to hypothesize that exogenous collagen can potentially promote wound healing by reducing degradation enzymes in the wound environment and disrupting the cycle of chronicity. Therefore, this study aimed to develop an optimal combination of fish collagen (FCOL), sodium alginate (SA), and hyaluronic acid (HA) loaded with bovine serum albumin (BSA) as a model protein fabricated as lyophilized scaffolds. The effects of sodium alginate (SA#) with higher mannuronic acid (M) were compared to sodium alginate (SA*) with higher guluronic acid (G). The SA* with higher G resulted in elegant scaffolds with hardness ranging from 3.74 N-4.29 N that were able to withstand the external force due to the glycosidic bonds in guluronic acid. Furthermore, the high G content also had a significant effect on the pore size, pore shape, and porosity. The water absorption (WA) ranged from 380-1382 (%) and equilibrium water content (EWC) 79-94 (%) after 24 h incubation at 37 °C. The SA* did not affect the water vapor transmission rate (WVTR) but incorporating BSA significantly increased the WVTR making these wound dressing scaffolds capable of absorbing about 50% exudate from a heavily exuding chronic wound. The protein released from the composite systems was best explained by the Korsmeyer-Peppas model with regression R2 values ranging from 0.896 to 0.971 and slope or n < 0.5 indicating that the BSA release mechanism was governed by quasi-Fickian diffusion. Cell viability assay showed that the scaffolds did not inhibit the proliferation of human dermal fibroblasts and human epidermal keratinocytes, and are therefore biocompatible. In vitro blood analysis using human whole blood confirmed that the BSA-loaded SA*:FCOL:HA scaffolds reduced the blood clotting index (BCI) by up to 20% compared to a commercially available sponge for chronic wounds. These features confirm that SA*:FCOL:HA scaffolds could be applied as a multifunctional wound dressing.},
}
@article {pmid35457787,
year = {2022},
author = {Yamatsu, K and Narazaki, K},
title = {Feasibility of the Remote Physical Activity Follow-Up Intervention after the Face-to-Face Program for Healthy Middle-Aged Adults: A Randomized Trial Using ICT and Mobile Technology.},
journal = {International journal of environmental research and public health},
volume = {19},
number = {8},
pages = {},
pmid = {35457787},
issn = {1660-4601},
support = {20K11446//Ministry of Education, Culture, Sports, Science and Technology/ ; 20H04016//Ministry of Education, Culture, Sports, Science and Technology/ ; 20H04016//Ministry of Education, Culture, Sports, Science and Technology/ ; 20H04030//Ministry of Education, Culture, Sports, Science and Technology/ ; },
mesh = {Adult ; *Exercise ; Feasibility Studies ; Follow-Up Studies ; Humans ; Middle Aged ; *Motor Activity ; Technology ; },
abstract = {Although the effectiveness of face-to-face and remote intervention for increasing and maintaining physical activity (PA) have been compared, the effect of combining the two forms of intervention is unknown. The purpose of this study was to examine the feasibility of the remote PA follow-up intervention after the face-to-face PA program on changing PA behaviors and some health outcomes in healthy middle-aged adults. As a secondary analysis, we also attempted a preliminary analysis of the difference in the number of behavior change interviews in the remote PA follow-up intervention. After the face-to-face intervention, 30 healthy subjects were randomly divided into four behavior change coaching interviews (BCI4 group) or three BCI (BCI3 group). The results of this study showed that body weight, body fat mass, and waist circumference were significantly reduced after face-to-face intervention, and were further reduced after remote PA follow-up intervention. However, the difference in the number of BCI affected only body fat mass. The remote PA follow-up intervention may have potential to maintain the effects of face-to-face intervention. In the future, it is necessary to refine the research design and conduct a full-scale intervention study.},
}
@article {pmid35456505,
year = {2022},
author = {Yin, Z and Guo, X and Qi, Y and Li, P and Liang, S and Xu, X and Shang, X},
title = {Dietary Restriction and Rapamycin Affect Brain Aging in Mice by Attenuating Age-Related DNA Methylation Changes.},
journal = {Genes},
volume = {13},
number = {4},
pages = {},
pmid = {35456505},
issn = {2073-4425},
support = {U1811262//National Natural Science Foundation of China/ ; 61772426//National Natural Science Foundation of China/ ; 2020AAA0108500//Key Research and Development Program of China/ ; 61802352//National Natural Science Foundation of China under Grant/ ; 2021GGJS095//Program for Young Key Teachers of Henan Province under Grant/ ; 2021ZDPY0208//Project of collaborative innovation in Zhengzhou under Grant/ ; },
mesh = {Aging/genetics/metabolism ; Animals ; *DNA Methylation ; Epigenesis, Genetic ; Hippocampus ; Mice ; *Sirolimus/pharmacology ; },
abstract = {The fact that dietary restriction (DR) and long-term rapamycin treatment (RALL) can ameliorate the aging process has been reported by many researchers. As the interface between external and genetic factors, epigenetic modification such as DNA methylation may have latent effects on the aging rate at the molecular level. To understand the mechanism behind the impacts of dietary restriction and rapamycin on aging, DNA methylation and gene expression changes were measured in the hippocampi of different-aged mice. Examining the single-base resolution of DNA methylation, we discovered that both dietary restriction and rapamycin treatment can maintain DNA methylation in a younger state compared to normal-aged mice. Through functional enrichment analysis of genes in which DNA methylation or gene expression can be affected by DR/RALL, we found that DR/RALL may retard aging through a relationship in which DNA methylation and gene expression work together not only in the same gene but also in the same biological process. This study is instructive for understanding the maintenance of DNA methylation by DR/RALL in the aging process, as well as the role of DR and RALL in the amelioration of aging.},
}
@article {pmid35454265,
year = {2022},
author = {Li, M and Fan, J and Lin, L and Shang, Z and Wan, H},
title = {Elevated Gamma Connectivity in Nidopallium Caudolaterale of Pigeons during Spatial Path Adjustment.},
journal = {Animals : an open access journal from MDPI},
volume = {12},
number = {8},
pages = {},
pmid = {35454265},
issn = {2076-2615},
support = {61673353//National Natural Science Foundation of China/ ; },
abstract = {Previous studies showed that spatial navigation depends on a local network including multiple brain regions with strong interactions. However, it is still not fully understood whether and how the neural patterns in avian nidopallium caudolaterale (NCL), which is suggested to play a key role in navigation as a higher cognitive structure, are modulated by the behaviors during spatial navigation, especially involved path adjustment needs. Hence, we examined neural activity in the NCL of pigeons and explored the local field potentials' (LFPs) spectral and functional connectivity patterns in a goal-directed spatial cognitive task with the detour paradigm. We found the pigeons progressively learned to solve the path adjustment task when the learned path was blocked suddenly. Importantly, the behavioral changes during the adjustment were accompanied by the modifications in neural patterns in the NCL. Specifically, the spectral power in lower bands (1-4 Hz and 5-12 Hz) decreased as the pigeons were tested during the adjustment. Meanwhile, an elevated gamma (31-45 Hz and 55-80 Hz) connectivity in the NCL was also detected. These results and the partial least square discriminant analysis (PLS-DA) modeling analysis provide insights into the neural activities in the avian NCL during the spatial path adjustment, contributing to understanding the potential mechanism of avian spatial encoding. This study suggests the important role of the NCL in spatial learning, especially path adjustment in avian navigation.},
}
@article {pmid35454043,
year = {2022},
author = {Altuwaijri, GA and Muhammad, G and Altaheri, H and Alsulaiman, M},
title = {A Multi-Branch Convolutional Neural Network with Squeeze-and-Excitation Attention Blocks for EEG-Based Motor Imagery Signals Classification.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {12},
number = {4},
pages = {},
pmid = {35454043},
issn = {2075-4418},
support = {RSP-2021/34//Researchers Supporting Project number (RSP-2021/34), King Saud University, Riyadh, Saudi Ara-bia/ ; },
abstract = {Electroencephalography-based motor imagery (EEG-MI) classification is a critical component of the brain-computer interface (BCI), which enables people with physical limitations to communicate with the outside world via assistive technology. Regrettably, EEG decoding is challenging because of the complexity, dynamic nature, and low signal-to-noise ratio of the EEG signal. Developing an end-to-end architecture capable of correctly extracting EEG data's high-level features remains a difficulty. This study introduces a new model for decoding MI known as a Multi-Branch EEGNet with squeeze-and-excitation blocks (MBEEGSE). By clearly specifying channel interdependencies, a multi-branch CNN model with attention blocks is employed to adaptively change channel-wise feature responses. When compared to existing state-of-the-art EEG motor imagery classification models, the suggested model achieves good accuracy (82.87%) with reduced parameters in the BCI-IV2a motor imagery dataset and (96.15%) in the high gamma dataset.},
}
@article {pmid35452895,
year = {2022},
author = {Filippini, M and Borra, D and Ursino, M and Magosso, E and Fattori, P},
title = {Decoding sensorimotor information from superior parietal lobule of macaque via Convolutional Neural Networks.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {151},
number = {},
pages = {276-294},
doi = {10.1016/j.neunet.2022.03.044},
pmid = {35452895},
issn = {1879-2782},
abstract = {Despite the well-recognized role of the posterior parietal cortex (PPC) in processing sensory information to guide action, the differential encoding properties of this dynamic processing, as operated by different PPC brain areas, are scarcely known. Within the monkey's PPC, the superior parietal lobule hosts areas V6A, PEc, and PE included in the dorso-medial visual stream that is specialized in planning and guiding reaching movements. Here, a Convolutional Neural Network (CNN) approach is used to investigate how the information is processed in these areas. We trained two macaque monkeys to perform a delayed reaching task towards 9 positions (distributed on 3 different depth and direction levels) in the 3D peripersonal space. The activity of single cells was recorded from V6A, PEc, PE and fed to convolutional neural networks that were designed and trained to exploit the temporal structure of neuronal activation patterns, to decode the target positions reached by the monkey. Bayesian Optimization was used to define the main CNN hyper-parameters. In addition to discrete positions in space, we used the same network architecture to decode plausible reaching trajectories. We found that data from the most caudal V6A and PEc areas outperformed PE area in the spatial position decoding. In all areas, decoding accuracies started to increase at the time the target to reach was instructed to the monkey, and reached a plateau at movement onset. The results support a dynamic encoding of the different phases and properties of the reaching movement differentially distributed over a network of interconnected areas. This study highlights the usefulness of neurons' firing rate decoding via CNNs to improve our understanding of how sensorimotor information is encoded in PPC to perform reaching movements. The obtained results may have implications in the perspective of novel neuroprosthetic devices based on the decoding of these rich signals for faithfully carrying out patient's intentions.},
}
@article {pmid35451963,
year = {2022},
author = {Andrews, A},
title = {Integration of Augmented Reality and Brain-Computer Interface Technologies for Health Care Applications: Exploratory and Prototyping Study.},
journal = {JMIR formative research},
volume = {6},
number = {4},
pages = {e18222},
doi = {10.2196/18222},
pmid = {35451963},
issn = {2561-326X},
abstract = {BACKGROUND: Augmented reality (AR) and brain-computer interface (BCI) are promising technologies that have a tremendous potential to revolutionize health care. While there has been a growing interest in these technologies for medical applications in the recent years, the combined use of AR and BCI remains a fairly unexplored area that offers significant opportunities for improving health care professional education and clinical practice. This paper describes a recent study to explore the integration of AR and BCI technologies for health care applications.<br><br>OBJECTIVE: The described effort aims to advance an understanding of how AR and BCI technologies can effectively work together to transform modern health care practice by providing new mechanisms to improve patient and provider learning, communication, and shared decision-making.<br><br>METHODS: The study methods included an environmental scan of AR and BCI technologies currently used in health care, a use case analysis for a combined AR-BCI capability, and development of an integrated AR-BCI prototype solution for health care applications.<br><br>RESULTS: The study resulted in a novel interface technology solution that enables interoperability between consumer-grade wearable AR and BCI devices and provides the users with an ability to control digital objects in augmented reality using neural commands. The article discusses this novel solution within the context of practical digital health use cases developed during the course of the study where the combined AR and BCI technologies are anticipated to produce the most impact.<br><br>CONCLUSIONS: As one of the pioneering efforts in the area of AR and BCI integration, the study presents a practical implementation pathway for AR-BCI integration and provides directions for future research and innovation in this area.},
}
@article {pmid35451087,
year = {2022},
author = {Loebner, HA and Volken, W and Mueller, S and Bertholet, J and Mackeprang, PH and Guyer, G and Aebersold, DM and Stampanoni, M and Manser, P and Fix, MK},
title = {Development of a Monte Carlo based robustness calculation and evaluation tool.},
journal = {Medical physics},
volume = {},
number = {},
pages = {},
doi = {10.1002/mp.15683},
pmid = {35451087},
issn = {2473-4209},
abstract = {BACKGROUND: Evaluating plan robustness is a key step in radiotherapy.<br><br>PURPOSE: To develop a flexible Monte Carlo (MC)-based robustness calculation and evaluation tool to assess and quantify dosimetric robustness of intensity modulated radiotherapy treatment plans by exploring the impact of systematic and random uncertainties resulting from patient setup, patient anatomy changes, and mechanical limitations of machine components.<br><br>METHODS: The robustness tool consists of two parts: the first part includes automated MC dose calculation of multiple user-defined uncertainty scenarios to populate a robustness space. An uncertainty scenario is defined by a certain combination of uncertainties in patient setup, rigid intra-fraction motion and in mechanical steering of the following machine components: angles of gantry, collimator, table-yaw, table-pitch, table-roll, translational positions of jaws, multi-leaf-collimator (MLC) banks, and single MLC leaves. The Swiss Monte Carlo Plan (SMCP) is integrated in this tool to serve as the backbone for the MC dose calculations incorporating the uncertainties. The calculated dose distributions serve as input for the second part of the tool, handling the quantitative evaluation of the dosimetric impact of the uncertainties. A graphical user interface (GUI) is developed to simultaneously evaluate the uncertainty scenarios according to user-specified conditions based on dose-volume histogram (DVH) parameters, fast and exact gamma analysis, and dose differences. Additionally, a robustness index (RI) is introduced with the aim to simultaneously evaluate and condense dosimetric robustness against multiple uncertainties into one number. The RI is defined as the ratio of scenarios passing the conditions on the dose distributions. Weighting of the scenarios in the robustness space is possible to consider their likelihood of occurrence. The robustness tool is applied on an intensity modulated radiotherapy (IMRT), a volumetric modulated arc therapy (VMAT), a dynamic trajectory radiotherapy (DTRT) and a dynamic mixed beam radiotherapy (DYMBER) plan for a brain case to evaluate the robustness to uncertainties of gantry-, table-, collimator angle, MLC, and intra-fraction motion. Additionally, the robustness of the IMRT, VMAT and DTRT plan against patient setup uncertainties are compared. The robustness tool is validated by Delta4 measurements for scenarios including all uncertainty types available.<br><br>RESULTS: The robustness tool performs simultaneous calculation of uncertainty scenarios, and the GUI enables their fast evaluation. For all evaluated plans and uncertainties, the PTV margin prevented major clinical target volume (CTV) coverage deterioration (maximum observed standard deviation of D98%CTV was 1.3 Gy). OARs close to the PTV experienced larger dosimetric deviations (maximum observed standard deviation of D2%chiasma was 14.5 Gy). Robustness comparison by RI evaluation against patient setup uncertainties revealed better dosimetric robustness of the VMAT and DTRT plans as compared to the IMRT plan. Delta4 validation measurements agreed with calculations by >96% gamma-passing rate (3%/2 mm).<br><br>CONCLUSION: The robustness tool was successfully implemented. Calculation and evaluation of uncertainty scenarios with the robustness tool were demonstrated on a brain case. Effects of patient and machine specific uncertainties and the combination thereof on the dose distribution are evaluated in a user-friendly GUI to quantitatively assess and compare treatment plans and their robustness. This article is protected by copyright. All rights reserved.},
}
@article {pmid35450498,
year = {2022},
author = {Qu, H and Zeng, F and Tang, Y and Shi, B and Wang, Z and Chen, X and Wang, J},
title = {The clinical effects of brain-computer interface with robot on upper-limb function for post-stroke rehabilitation: a meta-analysis and systematic review.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {},
number = {},
pages = {1-12},
doi = {10.1080/17483107.2022.2060354},
pmid = {35450498},
issn = {1748-3115},
abstract = {PURPOSE: Many recent clinical studies have suggested that the combination of brain-computer interfaces (BCIs) can induce neurological recovery and improvement in motor function. In this review, we performed a systematic review and meta-analysis to evaluate the clinical effects of BCI-robot systems.<br><br>METHODS: The articles published from January 2010 to December 2020 have been searched by using the databases (EMBASE, PubMed, CINAHL, EBSCO, Web of Science and manual search). The single-group studies were qualitatively described, and only the controlled-trial studies were included for the meta-analysis. The mean difference (MD) of Fugl-Meyer Assessment (FMA) scores were pooled and the random-effects model method was used to perform the meta-analysis. The PRISMA criteria were followed in current review.<br><br>RESULTS: A total of 897 records were identified, eight single-group studies and 11 controlled-trial studies were included in our review. The systematic analysis indicated that the BCI-robot systems had a significant improvement on motor function recovery. The meta-analysis showed there were no statistic differences between BCI-robot groups and robot groups, neither in the immediate effects nor long-term effects (p > 0.05).<br><br>CONCLUSION: The use of BCI-robot systems has significant improvement on the motor function recovery of hemiparetic upper-limb, and there is a sustaining effect. The meta-analysis showed no statistical difference between the experimental group (BCI-robot) and the control group (robot). However, there are a few shortcomings in the experimental design of existing studies, more clinical trials need to be conducted, and the experimental design needs to be more rigorous.Implications for RehabilitationIn this review, we evaluated the clinical effects of brain-computer interface with robot on upper-limb function for post-stroke rehabilitation. After we screened the databases, 19 articles were included in this review. These articles all clinical trial research, they all used non-invasive brain-computer interfaces and upper-limb robot.We conducted the systematic review with nine articles, the result indicated that the BCI-robot system had a significant improvement on motor function recovery. Eleven articles were included for the meta-analysis, the result showed there were no statistic differences between BCI-robot groups and robot groups, neither in the immediate effects nor long-term effects.We thought the result of meta-analysis which showed no statistic difference was probably caused by the heterogenicity of clinical trial designs of these articles.We thought the BCI-robot systems are promising strategies for post-stroke rehabilitation. And we gave several suggestions for further research: (1) The experimental design should be more rigorous, and describe the experimental designs in detail, especially the control group intervention, to make the experiment replicability. (2) New evaluation criteria need to be established, more objective assessment such as biomechanical assessment, fMRI should be utilised as the primary outcome. (3) More clinical studies with larger sample size, novel external devices, and BCI systems need to be conducted to investigate the differences between BCI-robot system and other interventions. (4) Further research could shift the focus to the patients who are in subacute stage, to explore if the early BCI training can make a positive impact on cerebral cortical recovery.},
}
@article {pmid35447619,
year = {2022},
author = {Atherton, E and Hu, Y and Brown, S and Papiez, E and Ling, V and Colvin, V and Borton, D},
title = {A 3D in vitro model of the device-tissue interface: Functional and structural symptoms of innate neuroinflammation are mitigated by antioxidant ceria nanoparticles.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ac6908},
pmid = {35447619},
issn = {1741-2552},
abstract = {OBJECTIVE: The recording instability of neural implants due to neuroinflammation at the device-tissue interface is a primary roadblock to broad adoption of brain-machine interfaces. While a multiphasic immune response, marked by glial scaring, oxidative stress (OS), and neurodegeneration, is well-characterized, the independent contributions of systemic and local "innate" immune responses are not well-understood. We aimed to understand and mitigate the isolated the innate neuroinflammatory response to devices.<br><br>APPROACH: Three-dimensional primary neural cultures provide a unique environment for studying the drivers of neuroinflammation by decoupling the innate and systemic immune systems, while conserving an endogenous extracellular matrix and structural and functional network complexity. We created a three-dimensional in vitro model of the DTI by seeding primary cortical cells around microwires. Live imaging of both dye and AAV-mediated functional, structural, and lipid peroxidation fluorescence was employed to characterize the neuroinflammatory response.<br><br>MAIN RESULTS: Live imaging of microtissues over time revealed independent innate neuroinflammation, marked by increased OS, decreased neuronal density, and increased functional connectivity. We demonstrated the use of this model for therapeutic screening by directly applying drugs to neural tissue, bypassing low bioavailability through the in vivo blood brain barrier. As there is growing interest in long-acting antioxidant therapies, we tested efficacy of "perpetual" antioxidant ceria nanoparticles, which reduced OS, increased neuronal density, and protected functional connectivity.<br><br>SIGNIFICANCE: Our 3D in vitro model of the device-tissue interface exhibited symptoms of OS-mediated innate neuroinflammation, indicating a significant local immune response to devices. The dysregulation of functional connectivity of microcircuits surround implants suggests the presence of an observer effect, in which the process of recording neural activity may fundamentally change the neural signal. Finally, the demonstration of antioxidant ceria nanoparticle treatment exhibited substantial promise as a neuroprotective and anti-inflammatory treatment strategy.},
}
@article {pmid35444515,
year = {2022},
author = {Singanamalla, SKR and Lin, CT},
title = {Spike-Representation of EEG Signals for Performance Enhancement of Brain-Computer Interfaces.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {792318},
pmid = {35444515},
issn = {1662-4548},
abstract = {Brain-computer interfaces (BCI) relying on electroencephalography (EEG) based neuroimaging mode has shown prospects for real-world usage due to its portability and optional selectivity of fewer channels for compactness. However, noise and artifacts often limit the capacity of BCI systems especially for event-related potentials such as P300 and error-related negativity (ERN), whose biomarkers are present in short time segments at the time-series level. Contrary to EEG, invasive recording is less prone to noise but requires a tedious surgical procedure. But EEG signal is the result of aggregation of neuronal spiking information underneath the scalp surface and transforming the relevant BCI task's EEG signal to spike representation could potentially help improve the BCI performance. In this study, we designed an approach using a spiking neural network (SNN) which is trained using surrogate-gradient descent to generate task-related multi-channel EEG template signals of all classes. The trained model is in turn leveraged to obtain the latent spike representation for each EEG sample. Comparing the classification performance of EEG signal and its spike-representation, the proposed approach enhanced the performance of ERN dataset from 79.22 to 82.27% with naive bayes and for P300 dataset, the accuracy was improved from 67.73 to 69.87% using xGboost. In addition, principal component analysis and correlation metrics were evaluated on both EEG signals and their spike-representation to identify the reason for such improvement.},
}
@article {pmid35444245,
year = {2022},
author = {Darbin, O and Hatanaka, N and Takara, S and Kaneko, N and Chiken, S and Naritoku, D and Martino, A and Nambu, A},
title = {Subthalamic nucleus deep brain stimulation driven by primary motor cortex γ2 activity in parkinsonian monkeys.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {6493},
pmid = {35444245},
issn = {2045-2322},
support = {15H05873//Ministry of Education, Culture, Sports, Science and Technology/ ; 19KK0193, 26250009//Japan Society for the Promotion of Science/ ; JP20dm0207050//Japan Agency for Medical Research and Development/ ; },
mesh = {Animals ; *Deep Brain Stimulation/methods ; Haplorhini ; *Motor Cortex/physiology ; *Parkinsonian Disorders ; *Subthalamic Nucleus/physiology ; },
abstract = {In parkinsonism, subthalamic nucleus (STN) electrical deep brain stimulation (DBS) improves symptoms, but may be associated with side effects. Adaptive DBS (aDBS), which enables modulation of stimulation, may limit side effects, but limited information is available about clinical effectiveness and efficaciousness. We developed a brain-machine interface for aDBS, which enables modulation of stimulation parameters of STN-DBS in response to γ2 band activity (80-200 Hz) of local field potentials (LFPs) recorded from the primary motor cortex (M1), and tested its effectiveness in parkinsonian monkeys. We trained two monkeys to perform an upper limb reaching task and rendered them parkinsonian with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. Bipolar intracortical recording electrodes were implanted in the M1, and a recording chamber was attached to access the STN. In aDBS, the M1 LFPs were recorded, filtered into the γ2 band, and discretized into logic pulses by a window discriminator, and the pulses were used to modulate the interval and amplitude of DBS pulses. In constant DBS (cDBS), constant stimulus intervals and amplitudes were used. Reaction and movement times during the task were measured and compared between aDBS and cDBS. The M1-γ2 activities were increased before and during movements in parkinsonian monkeys and these activities modulated the aDBS pulse interval, amplitude, and dispersion. With aDBS and cDBS, reaction and movement times were significantly decreased in comparison to DBS-OFF. The electric charge delivered was lower with aDBS than cDBS. M1-γ2 aDBS in parkinsonian monkeys resulted in clinical benefits that did not exceed those from cDBS. However, M1-γ2 aDBS achieved this magnitude of benefit for only two thirds of the charge delivered by cDBS. In conclusion, M1-γ2 aDBS is an effective therapeutic approach which requires a lower electrical charge delivery than cDBS for comparable clinical benefits.},
}
@article {pmid35443233,
year = {2022},
author = {Pulferer, HS and Ásgeirsdóttir, B and Mondini, V and Sburlea, AI and Müller-Putz, GR},
title = {Continuous 2D trajectory decoding from attempted movement: across-session performance in able-bodied and feasibility in a spinal cord injured participant.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ac689f},
pmid = {35443233},
issn = {1741-2552},
abstract = {OBJECTIVE: In people with a cervical spinal cord injury (SCI) or degenerative diseases leading to limited motor function, restoration of upper limb movement has been a goal of the brain-computer interface (BCI) field for decades. Recently, research from our group investigated non-invasive and real-time decoding of continuous movement in able-bodied participants from low-frequency brain signals during a target-tracking task. To advance our setup towards motor-impaired end users, we consequently chose a new paradigm based on attempted movement.<br><br>APPROACH: Here, we present the results of two studies. During the first study, data of ten able-bodied participants completing a target-tracking/shape-tracing task on-screen were investigated in terms of improvements in decoding performance due to user training. In a second study, a spinal cord injured participant underwent the same tasks. To investigate the merit of employing attempted movement in end users with SCI, data of the spinal cord injured participant were recorded twice; once within an observation only condition, and once while simultaneously attempting movement.<br><br>MAIN RESULTS: We observed mean correlation well above chance level for continuous motor decoding based on attempted movement in able-bodied participants. No global improvement over three sessions, both in sensor and source space, could be observed across all participants and movement parameters. In the participant with SCI, decoding performance well above chance was found.<br><br>SIGNIFICANCE: No presence of a learning effect in continuous attempted movement decoding in able-bodied participants could be observed. In contrast, non-significantly varying decoding patterns may promote the use of source space decoding in terms of generalized decoders utilizing transfer learning. Furthermore, above-chance correlations for attempted movement decoding ranging between those of observation only and executed movement were seen in one spinal cord injured participant, suggesting attempted movement decoding as a possible link between feasibility studies in able-bodied and actual applications in motor impaired end users.},
}
@article {pmid35442890,
year = {2022},
author = {Lim, H and Kim, S and Ku, J},
title = {Distraction classification during target tracking tasks involving target and cursor flickering using EEGNet.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2022.3168829},
pmid = {35442890},
issn = {1558-0210},
abstract = {Keeping patients from being distracted while performing motor rehabilitation is important. An EEG-based biofeedback strategy has been introduced to help encourage participants to focus their attention on rehabilitation tasks. Here, we suggest a BCI-based monitoring method using a flickering cursor and target that can evoke a steady-state visually evoked potential (SSVEP) using the fact that the SSVEP is modulated by a patient's attention. Fifteen healthy individuals performed a tracking task where the target and cursor flickered. There were two tracking sessions, one with and one without flickering stimuli, and each session had four conditions in which each had no distractor (non-D), a visual (vis-D) or cognitive distractor (cog-D), and both distractors (both-D). An EEGNet was trained as a classifier using only non-D and both-D conditions to classify whether it was distracted and validated with a leave-one-subject-out scheme. The results reveal that the proposed classifier demonstrates superior performance when using data from the task with the flickering stimuli compared to the case without the flickering stimuli. Furthermore, the observed classification likelihood was between those corresponding to the non-D and both-D when using the trained EEGNet. This suggests that the classifier trained for the two conditions could also be used to measure the level of distraction by windowing and averaging the outcomes. Therefore, the proposed method is advantageous because it can reveal a robust and continuous level of patient distraction. This facilitates its successful application to the rehabilitation systems that use computerized technology, such as virtual reality to encourage patient engagement.},
}
@article {pmid35441936,
year = {2022},
author = {Wilson, BS and Tucci, DL and Moses, DA and Chang, EF and Young, NM and Zeng, FG and Lesica, NA and Bur, AM and Kavookjian, H and Mussatto, C and Penn, J and Goodwin, S and Kraft, S and Wang, G and Cohen, JM and Ginsburg, GS and Dawson, G and Francis, HW},
title = {Harnessing the Power of Artificial Intelligence in Otolaryngology and the Communication Sciences.},
journal = {Journal of the Association for Research in Otolaryngology : JARO},
volume = {},
number = {},
pages = {},
pmid = {35441936},
issn = {1438-7573},
abstract = {Use of artificial intelligence (AI) is a burgeoning field in otolaryngology and the communication sciences. A virtual symposium on the topic was convened from Duke University on October 26, 2020, and was attended by more than 170 participants worldwide. This review presents summaries of all but one of the talks presented during the symposium; recordings of all the talks, along with the discussions for the talks, are available at https://www.youtube.com/watch?v=ktfewrXvEFg and https://www.youtube.com/watch?v=-gQ5qX2v3rg . Each of the summaries is about 2500 words in length and each summary includes two figures. This level of detail far exceeds the brief summaries presented in traditional reviews and thus provides a more-informed glimpse into the power and diversity of current AI applications in otolaryngology and the communication sciences and how to harness that power for future applications.},
}
@article {pmid35439750,
year = {2022},
author = {Gu, H and Yao, Q and Chen, H and Ding, Z and Zhao, X and Liu, H and Feng, Y and Li, C and Li, X},
title = {The effect of mental schema evolution on mental workload measurement: an EEG study with simulated quadrotor UAV operation.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ac6828},
pmid = {35439750},
issn = {1741-2552},
abstract = {OBJECTIVE: Mental workload is the result of the interactions between the demands of an operation task and the skills, behavior and perception of the performer. Working under a high mental workload can significantly affect an operator's ability to choose optimal decisions. However, the effect of mental schema, which reflects the level of expertise of an operator, on mental workload remains unclear. Here, we propose a theoretical framework for describing how the evolution of mental schema affects mental workload from the perspective of cognitive processing.<br><br>APPROACH: we recruited 51 students to participate in a 10-day simulated UAV flight training. The EEG PSD metrics were used to investigate the changes in neural responses caused by variations in the mental workload at different stages of mental schema evolution.<br><br>MAIN RESULTS: It was found that mental schema evolution influenced the direction and change trends of the frontal theta PSD, parietal alpha PSD, and central beta PSD. Initially, before the mental schema was formed, only the frontal theta PSD increased with increasing task difficulty; when the mental schema was initially being developed, the frontal theta PSD and the parietal alpha PSD decreased with increasing task difficulty, while the central beta PSD increased with increasing task difficulty. Finally, as the mental schema gradually matured, the trend of the three indicators did not change with increasing task difficulty. However, differences in the frontal PSD became more pronounced across task difficulty levels, while differences in the parietal PSD narrowed.<br><br>SIGNIFICANCE: Our results describe the relationship between the EEG power spectrum and the mental workload of UAV operators as the mental schema evolved. This suggests that EEG indicators can not only provide more accurate measurements of mental workload but also provide insights into the development of an operator's skill level.},
}
@article {pmid35439124,
year = {2022},
author = {Xia, K and Deng, L and Duch, W and Wu, D},
title = {Privacy-Preserving Domain Adaptation for Motor Imagery-based Brain-Computer Interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2022.3168570},
pmid = {35439124},
issn = {1558-2531},
abstract = {OBJECTIVE: Electroencephalogram (EEG) is one of the most widely used signals in motor imagery (MI) based brain-computer interfaces (BCIs). Domain adaptation has been frequently used to improve the accuracy of EEG-based BCIs for a new user (target domain), by making use of labeled data from a previous user (source domain). However, this raises privacy concerns, as EEG contains sensitive health and mental information. It is very important to perform privacy-preserving domain adaptation, which simultaneously improves the classification accuracy for a new user and protects the privacy of a previous user.<br><br>METHODS: We propose augmentation-based source-free adaptation (ASFA), which consists of two parts: 1) source model training, where a novel data augmentation approach is proposed for MI EEG signals to improve the cross-subject generalization performance of the source model; and, 2) target model training, which simultaneously considers uncertainty reduction for domain adaptation and consistency regularization for robustness. ASFA only needs access to the source model parameters, instead of the raw EEG data, thus protecting the privacy of the source domain. We further extend ASFA to a stricter privacy-preserving scenario, where the source model's parameters are also inaccessible.<br><br>RESULTS: Experimental results on four MI datasets demonstrated that ASFA outperformed 15 classical and state-of-the-art MI classification approaches.<br><br>SIGNIFICANCE: This is the first work on completely source-free domain adaptation for EEG-based BCIs. Our proposed ASFA achieves high classification accuracy and strong privacy protection simultaneously, important for the commercial applications of EEG-based BCIs.},
}
@article {pmid35439025,
year = {2022},
author = {Andre, F and Ismaila, N and Allison, KH and Barlow, WE and Collyar, DE and Damodaran, S and Henry, NL and Jhaveri, K and Kalinsky, K and Kuderer, NM and Litvak, A and Mayer, EL and Pusztai, L and Raab, R and Wolff, AC and Stearns, V},
title = {Biomarkers for Adjuvant Endocrine and Chemotherapy in Early-Stage Breast Cancer: ASCO Guideline Update.},
journal = {Journal of clinical oncology : official journal of the American Society of Clinical Oncology},
volume = {},
number = {},
pages = {JCO2200069},
doi = {10.1200/JCO.22.00069},
pmid = {35439025},
issn = {1527-7755},
abstract = {PURPOSE: To update recommendations on appropriate use of breast cancer biomarker assay results to guide adjuvant endocrine and chemotherapy decisions in early-stage breast cancer.<br><br>METHODS: An updated literature search identified randomized clinical trials and prospective-retrospective studies published from January 2016 to October 2021. Outcomes of interest included overall survival and disease-free or recurrence-free survival. Expert Panel members used informal consensus to develop evidence-based recommendations.<br><br>RESULTS: The search identified 24 studies informing the evidence base.<br><br>RECOMMENDATIONS: Clinicians may use Oncotype DX, MammaPrint, Breast Cancer Index (BCI), and EndoPredict to guide adjuvant endocrine and chemotherapy in patients who are postmenopausal or age > 50 years with early-stage estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative (ER+ and HER2-) breast cancer that is node-negative or with 1-3 positive nodes. Prosigna and BCI may be used in postmenopausal patients with node-negative ER+ and HER2- breast cancer. In premenopausal patients, clinicians may use Oncotype in patients with node-negative ER+ and HER2- breast cancer. Current data suggest that premenopausal patients with 1-3 positive nodes benefit from chemotherapy regardless of genomic assay result. There are no data on use of genomic tests to guide adjuvant chemotherapy in patients with ≥ 4 positive nodes. Ki67 combined with other parameters or immunohistochemistry 4 score may be used in postmenopausal patients without access to genomic tests to guide adjuvant therapy decisions. BCI may be offered to patients with 0-3 positive nodes who received 5 years of endocrine therapy without evidence of recurrence to guide decisions about extended endocrine therapy. None of the assays are recommended for treatment guidance in individuals with HER2-positive or triple-negative breast cancer. Treatment decisions should also consider disease stage, comorbidities, and patient preferences.Additional information is available at www.asco.org/breast-cancer-guidelines.},
}
@article {pmid35434211,
year = {2022},
author = {du Bois, N and Bigirimana, AD and Korik, A and Kéthina, LG and Rutembesa, E and Mutabaruka, J and Mutesa, L and Prasad, G and Jansen, S and Coyle, D},
title = {Electroencephalography and psychological assessment datasets to determine the efficacy of a low-cost, wearable neurotechnology intervention for reducing Post-Traumatic Stress Disorder symptom severity.},
journal = {Data in brief},
volume = {42},
number = {},
pages = {108066},
pmid = {35434211},
issn = {2352-3409},
abstract = {The datasets described here comprise electroencephalography (EEG) data and psychometric data freely available on data.mendeley.com. The EEG data is available in .mat formatted files containing the EEG signal values structured in two-dimensional (2D) matrices, with channel data and trigger information in rows, and samples in columns (having a sampling rate of 250Hz). Twenty-nine female survivors of the 1994 genocide against the Tutsi in Rwanda, underwent a psychological assessment before and after an intervention aimed at reducing Post-Traumatic Stress Disorder (PTSD) symptom severity. Three measures of trauma and four measures of wellbeing were assessed using empirically validated standardised assessments. The pre- and post- intervention psychometric data were analysed using non-parametric statistical methods and the post-intervention data were further evaluated according to diagnostic assessment rules to determine clinically relevant improvements for each group. The participants were assigned to a control group (CG, n = 9), a motor-imagery group (MI, n = 10), and a neurofeedback group (NF, n = 10). Participants in the latter two groups received Brain-Computer Interface (BCI) based training as a treatment intervention over a sixteen-day period, between the pre- and post- clinical interviews. The training involved presenting feedback visually via a videogame, based on real-time analysis of the EEG recorded data during the BCI-based treatment session. Participants were asked to regulate (NF) or intentionally modulate (MI) brain activity to affect/control the game.},
}
@article {pmid35433527,
year = {2022},
author = {Maghsoudi, A and Shalbaf, A},
title = {Hand Motor Imagery Classification Using Effective Connectivity and Hierarchical Machine Learning in EEG Signals.},
journal = {Journal of biomedical physics &amp; engineering},
volume = {12},
number = {2},
pages = {161-170},
pmid = {35433527},
issn = {2251-7200},
abstract = {Background: Motor Imagery (MI) Brain Computer Interface (BCI) directly links central nervous system to a computer or a device. Most MI-BCI structures rely on features of a single channel of Electroencephalogram (EEG). However, to provide more valuable features, the relationships among EEG channels in the form of effective brain connectivity analysis must be considered.<br><br>Objective: This study aims to identify a set of robust and discriminative effective connectivity features from EEG signals and to develop a hierarchical machine learning structure for discrimination of left and right hand MI task effectively.<br><br>Material and Methods: In this analytical study, we estimated effective connectivity using Granger Causality (GC) methods namely, Generalized Partial Directed Coherence (GPDC), Directed Transfer Function (DTF) and direct Directed Transfer Function (dDTF). These measures determine the transient causal relation between different brain areas. Then a feature subset selection method based on Kruskal-Wallis test was performed to choose most significant directed causal connection between channels. Moreover, the minimal-redundancy-maximal-relevance feature selection method is applied to discard non-significance features. Finally, support vector machine method is used for classification.<br><br>Results: The maximum value of the classification accuracies using GC methods over different frequency bands in 29 subjects in 60 trial is approximately 84% in Mu (8-12 Hz) - Beta1 (12 - 15 Hz) frequency band using GPDC method.<br><br>Conclusion: This new hierarchical automated BCI system could be applied for discrimination of left and right hand MI tasks from EEG signal, effectively.},
}
@article {pmid35431792,
year = {2022},
author = {Lin, Q and Zhang, Y and Zhang, Y and Zhuang, W and Zhao, B and Ke, X and Peng, T and You, T and Jiang, Y and Yilifate, A and Huang, W and Hou, L and You, Y and Huai, Y and Qiu, Y and Zheng, Y and Ou, H},
title = {The Frequency Effect of the Motor Imagery Brain Computer Interface Training on Cortical Response in Healthy Subjects: A Randomized Clinical Trial of Functional Near-Infrared Spectroscopy Study.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {810553},
pmid = {35431792},
issn = {1662-4548},
abstract = {Background: The motor imagery brain computer interface (MI-BCI) is now available in a commercial product for clinical rehabilitation. However, MI-BCI is still a relatively new technology for commercial rehabilitation application and there is limited prior work on the frequency effect. The MI-BCI has become a commercial product for clinical neurological rehabilitation, such as rehabilitation for upper limb motor dysfunction after stroke. However, the formulation of clinical rehabilitation programs for MI-BCI is lack of scientific and standardized guidance, especially limited prior work on the frequency effect. Therefore, this study aims at clarifying how frequency effects on MI-BCI training for the plasticity of the central nervous system.<br><br>Methods: Sixteen young healthy subjects (aged 22.94 ± 3.86 years) were enrolled in this randomized clinical trial study. Subjects were randomly assigned to a high frequency group (HF group) and low frequency group (LF group). The HF group performed MI-BCI training once per day while the LF group performed once every other day. All subjects performed 10 sessions of MI-BCI training. functional near-infrared spectroscopy (fNIRS) measurement, Wolf Motor Function Test (WMFT) and brain computer interface (BCI) performance were assessed at baseline, mid-assessment (after completion of five BCI training sessions), and post-assessment (after completion of 10 BCI training sessions).<br><br>Results: The results from the two-way ANOVA of beta values indicated that GROUP, TIME, and GROUP × TIME interaction of the right primary sensorimotor cortex had significant main effects [GROUP: F (1,14) = 7.251, P = 0.010; TIME: F (2,13) = 3.317, P = 0.046; GROUP × TIME: F (2,13) = 5.676, P = 0.007]. The degree of activation was affected by training frequency, evaluation time point and interaction. The activation of left primary sensory motor cortex was also affected by group (frequency) (P = 0.003). Moreover, the TIME variable was only significantly different in the HF group, in which the beta value of the mid-assessment was higher than that of both the baseline assessment (P = 0.027) and post-assessment (P = 0.001), respectively. Nevertheless, there was no significant difference in the results of WMFT between HF group and LF group.<br><br>Conclusion: The major results showed that more cortical activation and better BCI performance were found in the HF group relative to the LF group. Moreover, the within-group results also showed more cortical activation after five sessions of BCI training and better BCI performance after 10 sessions in the HF group, but no similar effects were found in the LF group. This pilot study provided an essential reference for the formulation of clinical programs for MI-BCI training in improvement for upper limb dysfunction.},
}
@article {pmid35431612,
year = {2022},
author = {Prasad, DS and Chanamallu, SR and Prasad, KS},
title = {Optimized deformable convolution network for detection and mitigation of ocular artifacts from EEG signal.},
journal = {Multimedia tools and applications},
volume = {},
number = {},
pages = {1-39},
pmid = {35431612},
issn = {1380-7501},
abstract = {Electroencephalogram (EEG) is the key component in the field of analyzing brain activity and behavior. EEG signals are affected by artifacts in the recorded electrical activity; thereby it affects the analysis of EGG. To extract the clean data from EEG signals and to improve the efficiency of detection during encephalogram recordings, a developed model is required. Although various methods have been proposed for the artifacts removal process, sill the research on this process continues. Even if, several types of artifacts from both the subject and equipment interferences are highly contaminated the EEG signals, the most common and important type of interferences is known as Ocular artifacts. Many applications like Brain-Computer Interface (BCI) need online and real-time processing of EEG signals. Hence, it is best if the removal of artifacts is performed in an online fashion. The main intention of this proposal is to accomplish the new deep learning-based ocular artifacts detection and prevention model. In the detection phase, the 5-level Discrete Wavelet Transform (DWT), and Pisarenko harmonic decomposition are used for decomposing the signals. Then, the Principle Component Analysis (PCA) and Independent Component Analysis (ICA) are adopted as the techniques for extracting the features. With the collected features, the development of optimized Deformable Convolutional Networks (DCN) is used for the detection of ocular artifacts from the input EEG signal. Here, the optimized DCN is developed by optimizing or tuning some significant parameters by Distance Sorted-Electric Fish Optimization (DS-EFO). If the artifacts are detected, the mitigation process is performed by applying the Empirical Mean Curve Decomposition (EMCD), and then, the optimized DCN is used for denoising the signals. Finally, the clean signal is generated by applying inverse EMCD. Based on the EEG data collected from diverse subjects, the proposed method has achieved a higher performance than that of conventional methods, which demonstrates a better ocular-artifact reduction by the proposed method.},
}
@article {pmid35427686,
year = {2022},
author = {Sun, Q and Zheng, L and Pei, W and Gao, X and Wang, Y},
title = {A 120-target brain-computer interface based on code-modulated visual evoked potentials.},
journal = {Journal of neuroscience methods},
volume = {375},
number = {},
pages = {109597},
doi = {10.1016/j.jneumeth.2022.109597},
pmid = {35427686},
issn = {1872-678X},
abstract = {BACKGROUND: In recent years, numerous studies on the brain-computer interface (BCI) have been published. However, the number of targets in most of the existing studies was not enough for many practical applications.<br><br>NEW METHOD: To achieve highly efficient communications, this study proposed a 120-target BCI system based on code-modulated visual evoked potentials (c-VEPs). Four 31-bit pseudorandom codes were used, and each code generated 30 targets by cyclic shift with a lag of 1 bit.<br><br>RESULTS: In the online experiments, subjects could select one target in 1.04 s (0.52 s for stimulation and 0.52 s for gaze shifting) with an average information transfer rate (ITR) of 265.74 bits/min.<br><br>The proposed system achieved more targets and higher ITR than other recent c-VEP based studies. which attributes to the optimal code combination and the 1-bit lag.<br><br>CONCLUSION: The results illustrate that the proposed BCI system can achieve a high ITR with a short stimulation time. In addition, the c-VEP paradigm can shorten the training time, which ensures practicality in real applications.},
}
@article {pmid35422693,
year = {2022},
author = {Peng, Y and Wang, J and Liu, Z and Zhong, L and Wen, X and Wang, P and Gong, X and Liu, H},
title = {The Application of Brain-Computer Interface in Upper Limb Dysfunction After Stroke: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {798883},
pmid = {35422693},
issn = {1662-5161},
abstract = {Objective: This study aimed to examine the effectiveness and safety of the Brain-computer interface (BCI) in treatment of upper limb dysfunction after stroke.<br><br>Methods: English and Chinese electronic databases were searched up to July 2021. Randomized controlled trials (RCTs) were eligible. The methodological quality was assessed using Cochrane's risk-of-bias tool. Meta-analysis was performed using RevMan 5.4.<br><br>Results: A total of 488 patients from 16 RCTs were included. The results showed that (1) the meta-analysis of BCI-combined treatment on the improvement of the upper limb function showed statistical significance [standardized mean difference (SMD): 0.53, 95% CI: 0.26-0.80, P < 0.05]; (2) BCI treatment can improve the abilities of daily living of patients after stroke, and the analysis results are statistically significant (SMD: 1.67, 95% CI: 0.61-2.74, P < 0.05); and (3) the BCI-combined therapy was not statistically significant for the analysis of the Modified Ashworth Scale (MAS) (SMD: -0.10, 95% CI: -0.50 to 0.30, P = 0.61).<br><br>Conclusion: The meta-analysis indicates that the BCI therapy or BCI combined with other therapies such as conventional rehabilitation training and motor imagery training can improve upper limb dysfunction after stroke and enhance the quality of daily life.},
}
@article {pmid35421857,
year = {2022},
author = {Hammer, J and Schirrmeister, RT and Hartmann, K and Marusic, P and Schulze-Bonhage, A and Ball, T},
title = {Interpretable functional specialization emerges in deep convolutional networks trained on brain signals.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ac6770},
pmid = {35421857},
issn = {1741-2552},
abstract = {OBJECTIVE: Functional specialization is fundamental to neural information processing. Here, we study whether and how functional specialization emerges in artificial deep convolutional neural networks (CNNs) during a brain-computer interfacing (BCI) task.<br><br>APPROACH: We trained CNNs to predict hand movement speed from intracranial EEG (iEEG) and delineated how units across the different CNN hidden layers learned to represent the iEEG signal.<br><br>MAIN RESULTS: We show that distinct, functionally interpretable neural populations emerged as a result of the training process. While some units became sensitive to either iEEG amplitude or phase, others showed bimodal behavior with significant sensitivity to both features. Pruning of highly-sensitive units resulted in a steep drop of decoding accuracy not observed for pruning of less sensitive units, highlighting the functional relevance of the amplitude- and phase-specialized populations.<br><br>SIGNIFICANCE: We anticipate that emergent functional specialization as uncovered here will become a key concept in research towards interpretable deep learning for neuroscience and BCI applications.},
}
@article {pmid35421817,
year = {2022},
author = {Zhao, S and Guan, W and Qi, G and Li, P},
title = {Heterogeneous overtaking and learning styles with varied EEG patterns in a reinforced driving task.},
journal = {Accident; analysis and prevention},
volume = {171},
number = {},
pages = {106665},
doi = {10.1016/j.aap.2022.106665},
pmid = {35421817},
issn = {1879-2057},
abstract = {Overtaking maneuvers occur when vehicle drivers pursue higher driving speeds or comfort scenarios through back-to-back lane-changing behaviors, which require active participation of mental resources and certain self-learning practices. However, few studies have investigated how brain activities change during overtaking. Moreover, the learning process, which indicates the heterogeneity of drivers from a process-based perspective, has been neglected. In this work, we studied varied overtaking and learning styles using electroencephalogram (EEG) signals collected from drivers during a simulated driving task with a possible learning process. The average speed, standard deviation of speed, steering wheel angle and lateral movement distance of overtaking behaviors are analyzed in these reinforced tasks to evaluate overtaking performance. Four types of overtaking styles (i.e., low-speed type, low-speed &amp; strong-oscillation type, high-speed &amp; strong-steering type, and high-speed &amp; close-distance type) and three types of learning styles (i.e., stable, adaptive and changeful) are discovered, not only from eventual overtaking behaviors but also from behavioral changes in a certain learning process. EEG features, such as the power spectral density (PSD) in the θ, α, β and γ bands, are extracted to characterize driver mental states and to correlate with heterogeneous learning styles. The obtained results show that fatigue and fatigue confrontation are more likely with a stable learning style, and the mental workload is reduced with an adaptive learning style, whereas no significant changes in brain activity are apparent with a changeful learning style. Understanding and recognizing heterogeneous overtaking and learning styles with varying EEG patterns will be extremely useful in the future for deep integration of advanced driving assistance systems (ADASs) and brain computer interface (BCI) systems.},
}
@article {pmid35420985,
year = {2022},
author = {Sadatnejad, K and Lotte, F},
title = {Riemannian channel selection for BCI with between-session non-stationarity reduction capabilities.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2022.3167262},
pmid = {35420985},
issn = {1558-0210},
abstract = {OBJECTIVE: Between-session non-stationarity is a major challenge of current Brain-Computer Interfaces (BCIs) that affects system performance. In this paper, we investigate the use of channel selection for reducing between-session non-stationarity with Riemannian BCI classifiers. We use the Riemannian geometry framework of covariance matrices due to its robustness and promising performances. Current Riemannian channel selection methods do not consider between-session non-stationarity and are usually tested on a single session. Here, we propose a new channel selection approach that specifically considers non-stationarity effects and is assessed on multi-session BCI data sets.<br><br>METHODS: We remove the least significant channels using a sequential floating backward selection search strategy. Our contributions include: 1) quantifying the non-stationarity effects on brain activity in multi-class problems by different criteria in a Riemannian framework and 2) a method to predict whether BCI performance can improve using channel selection.<br><br>RESULTS: We evaluate the proposed approaches on three multi-session and multi-class mental tasks (MT)-based BCI datasets. They could lead to significant improvements in performance as compared to using all channels for datasets affected by between-session non-stationarity and to significant superiority to the state-of-the-art Riemannian channel selection methods over all datasets, notably when selecting small channel set sizes.<br><br>CONCLUSION: Reducing non-stationarity by channel selection could significantly improve Riemannian BCI classification accuracy.<br><br>SIGNIFICANCE: Our proposed channel selection approach contributes to make Riemannian BCI classifiers more robust to between-session non-stationarities.},
}
@article {pmid35420003,
year = {2022},
author = {Fu, Y and Wang, F and Li, Y and Gong, A and Qian, Q and Su, L and Zhao, L},
title = {Real-time recognition of different imagined actions on the same side of a single limb based on the fNIRS correlation coefficient.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {},
number = {},
pages = {},
doi = {10.1515/bmt-2021-0422},
pmid = {35420003},
issn = {1862-278X},
abstract = {Functional near-infrared spectroscopy (fNIRS) is a type of functional brain imaging. Brain-computer interfaces (BCIs) based on fNIRS have recently been implemented. Most existing fNIRS-BCI studies have involved off-line analyses, but few studies used online performance testing. Furthermore, existing online fNIRS-BCI experimental paradigms have not yet carried out studies using different imagined movements of the same side of a single limb. In the present study, a real-time fNIRS-BCI system was constructed to identify two imagined movements of the same side of a single limb (right forearm and right hand). Ten healthy subjects were recruited and fNIRS signal was collected and real-time analyzed with two imagined movements (leftward movement involving the right forearm and right-hand clenching). In addition to the mean and slope features of fNIRS signals, the correlation coefficient between fNIRS signals induced by different imagined actions was extracted. A support vector machine (SVM) was used to classify the imagined actions. The average accuracy of real-time classification of the two imagined movements was 72.25 ± 0.004%. The findings suggest that different imagined movements on the same side of a single limb can be recognized real-time based on fNIRS, which may help to further guide the practical application of online fNIRS-BCIs.},
}
@article {pmid35418848,
year = {2022},
author = {Jiang, Y and Jessee, W and Hoyng, S and Borhani, S and Liu, Z and Zhao, X and Price, LK and High, W and Suhl, J and Cerel-Suhl, S},
title = {Sharpening Working Memory With Real-Time Electrophysiological Brain Signals: Which Neurofeedback Paradigms Work?.},
journal = {Frontiers in aging neuroscience},
volume = {14},
number = {},
pages = {780817},
pmid = {35418848},
issn = {1663-4365},
abstract = {Growing evidence supports the idea that the ultimate biofeedback is to reward sensory pleasure (e.g., enhanced visual clarity) in real-time to neural circuits that are associated with a desired performance, such as excellent memory retrieval. Neurofeedback is biofeedback that uses real-time sensory reward to brain activity associated with a certain performance (e.g., accurate and fast recall). Working memory is a key component of human intelligence. The challenges are in our current limited understanding of neurocognitive dysfunctions as well as in technical difficulties for closed-loop feedback in true real-time. Here we review recent advancements of real time neurofeedback to improve memory training in healthy young and older adults. With new advancements in neuromarkers of specific neurophysiological functions, neurofeedback training should be better targeted beyond a single frequency approach to include frequency interactions and event-related potentials. Our review confirms the positive trend that neurofeedback training mostly works to improve memory and cognition to some extent in most studies. Yet, the training typically takes multiple weeks with 2-3 sessions per week. We review various neurofeedback reward strategies and outcome measures. A well-known issue in such training is that some people simply do not respond to neurofeedback. Thus, we also review the literature of individual differences in psychological factors e.g., placebo effects and so-called "BCI illiteracy" (Brain Computer Interface illiteracy). We recommend the use of Neural modulation sensitivity or BCI insensitivity in the neurofeedback literature. Future directions include much needed research in mild cognitive impairment, in non-Alzheimer's dementia populations, and neurofeedback using EEG features during resting and sleep for memory enhancement and as sensitive outcome measures.},
}
@article {pmid35413050,
year = {2022},
author = {Korkmaz, OE and Aydemir, O and Oral, EA and Ozbek, IY},
title = {An efficient 3D column-only P300 speller paradigm utilizing few numbers of electrodes and flashings for practical BCI implementation.},
journal = {PloS one},
volume = {17},
number = {4},
pages = {e0265904},
pmid = {35413050},
issn = {1932-6203},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/methods ; Event-Related Potentials, P300/physiology ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {The event related P300 potentials, positive waveforms in electroencephalography (EEG) signals, are often utilized in brain computer interfaces (BCI). Many studies have been carried out to improve the performance of P300 speller systems either by developing signal processing algorithms and classifiers with different architectures or by designing new paradigms. In this study, a new paradigm is proposed for this purpose. The proposed paradigm combines two remarkable properties of being a 3D animation and utilizing column-only flashings as opposed to classical paradigms which are based on row-column flashings in 2D manner. The new paradigm is utilized in a traditional two-layer artificial neural networks model with a single output neuron, and numerous experiments are conducted to evaluate and compare the performance of the proposed paradigm with that of the classical approach. The experimental results, including statistical significance tests, are presented for single and multiple EEG electrode usage combinations in 1, 3 and 15 flashing repetitions to detect P300 waves as well as to recognize target characters. Using the proposed paradigm, the best average classification accuracy rates on the test data are improved from 89.97% to 93.90% (an improvement of 4.36%) for 1 flashing, from 97.11% to 98.10% (an improvement of 1.01%) for 3 flashings and from 99.70% to 99.81% (an improvement of 0.11%) for 15 flashings when all electrodes, included in the study, are utilized. On the other hand, the accuracy rates are improved by 9.69% for 1 flashing, 4.72% for 3 flashings and 1.73% for 15 flashings when the proposed paradigm is utilized with a single EEG electrode (P8). It is observed that the proposed speller paradigm is especially useful in BCI systems designed for few EEG electrodes usage, and hence, it is more suitable for practical implementations. Moreover, all participants, given a subjective test, declared that the proposed paradigm is more user-friendly than classical ones.},
}
@article {pmid35408306,
year = {2022},
author = {Montoya, D and Barria, P and Cifuentes, CA and Aycardi, LF and Morís, A and Aguilar, R and Azorín, JM and Múnera, M},
title = {Biomechanical Assessment of Post-Stroke Patients' Upper Limb before and after Rehabilitation Therapy Based on FES and VR.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {7},
pages = {},
pmid = {35408306},
issn = {1424-8220},
mesh = {Electric Stimulation/methods ; Humans ; Recovery of Function ; *Stroke ; *Stroke Rehabilitation/methods ; Upper Extremity ; *Virtual Reality ; },
abstract = {Stroke is a medical condition characterized by the rapid loss of focal brain function. Post-stroke patients attend rehabilitation training to prevent the degeneration of physical function and improve upper limb movements and functional status after stroke. Promising rehabilitation therapies include functional electrical stimulation (FES), exergaming, and virtual reality (VR). This work presents a biomechanical assessment of 13 post-stroke patients with hemiparesis before and after rehabilitation therapy for two months with these three methods. Patients performed two tests (Maximum Forward Reach and Apley Scratching) where maximum angles, range of motion, angular velocities, and execution times were measured. A Wilcoxon test was performed (p = 0.05) to compare the variables before and after the therapy for paretic and non-paretic limbs. Significant differences were found in range of motion in flexion-extension, adduction-abduction, and internal-external rotation of the shoulder. Increases were found in flexion-extension, 17.98%, and internal-external rotation, 18.12%, after therapy in the Maximum Forward Reach Test. For shoulder adduction-abduction, the increase found was 20.23% in the Apley Scratching Test, supporting the benefits of rehabilitation therapy that combines FES, exergaming, and VR in the literature.},
}
@article {pmid35408190,
year = {2022},
author = {Gulraiz, A and Naseer, N and Nazeer, H and Khan, MJ and Khan, RA and Shahbaz Khan, U},
title = {LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {7},
pages = {},
pmid = {35408190},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Spectroscopy, Near-Infrared/methods ; Support Vector Machine ; Walking ; },
abstract = {Brain-computer interface (BCI) systems based on functional near-infrared spectroscopy (fNIRS) have been used as a way of facilitating communication between the brain and peripheral devices. The BCI provides an option to improve the walking pattern of people with poor walking dysfunction, by applying a rehabilitation process. A state-of-the-art step-wise BCI system includes data acquisition, pre-processing, channel selection, feature extraction, and classification. In fNIRS-based BCI (fNIRS-BCI), channel selection plays a vital role in enhancing the classification accuracy of the BCI problem. In this study, the concentration of blood oxygenation (HbO) in a resting state and in a walking state was used to decode the walking activity and the resting state of the subject, using channel selection by Least Absolute Shrinkage and Selection Operator (LASSO) homotopy-based sparse representation classification. The fNIRS signals of nine subjects were collected from the left hemisphere of the primary motor cortex. The subjects performed the task of walking on a treadmill for 10 s, followed by a 20 s rest. Appropriate filters were applied to the collected signals to remove motion artifacts and physiological noises. LASSO homotopy-based sparse representation was used to select the most significant channels, and then classification was performed to identify walking and resting states. For comparison, the statistical spatial features of mean, peak, variance, and skewness, and their combination, were used for classification. The classification results after channel selection were then compared with the classification based on the extracted features. The classifiers used for both methods were linear discrimination analysis (LDA), support vector machine (SVM), and logistic regression (LR). The study found that LASSO homotopy-based sparse representation classification successfully discriminated between the walking and resting states, with a better average classification accuracy (p < 0.016) of 91.32%. This research provides a step forward in improving the classification accuracy of fNIRS-BCI systems. The proposed methodology may also be used for rehabilitation purposes, such as controlling wheelchairs and prostheses, as well as an active rehabilitation training technique for patients with motor dysfunction.},
}
@article {pmid35408182,
year = {2022},
author = {Karimi, R and Mohammadi, A and Asif, A and Benali, H},
title = {DF-SSmVEP: Dual Frequency Aggregated Steady-State Motion Visual Evoked Potential Design with Bifold Canonical Correlation Analysis.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {7},
pages = {},
pmid = {35408182},
issn = {1424-8220},
support = {RGPIN-2016-04988//Natural Sciences and Engineering Research Council/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Canonical Correlation Analysis ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Photic Stimulation/methods ; Rotation ; },
abstract = {Recent advancements in Electroencephalographic (EEG) sensor technologies and signal processing algorithms have paved the way for further evolution of Brain Computer Interfaces (BCI) in several practical applications, ranging from rehabilitation systems to smart consumer technologies. When it comes to Signal Processing (SP) for BCI, there has been a surge of interest on Steady-State motion Visual Evoked Potentials (SSmVEP), where motion stimulation is used to address key issues associated with conventional light flashing/flickering. Such benefits, however, come with the price of being less accurate and having a lower Information Transfer Rate (ITR). From this perspective, this paper focuses on the design of a novel SSmVEP paradigm without using resources such as trial time, phase, and/or number of targets to enhance the ITR. The proposed design is based on the intuitively pleasing idea of integrating more than one motion within a single SSmVEP target stimuli, simultaneously. To elicit SSmVEP, we designed a novel and innovative dual frequency aggregated modulation paradigm, called the Dual Frequency Aggregated Steady-State motion Visual Evoked Potential (DF-SSmVEP), by concurrently integrating "Radial Zoom" and "Rotation" motions in a single target without increasing the trial length. Compared to conventional SSmVEPs, the proposed DF-SSmVEP framework consists of two motion modes integrated and shown simultaneously each modulated by a specific target frequency. The paper also develops a specific unsupervised classification model, referred to as the Bifold Canonical Correlation Analysis (BCCA), based on two motion frequencies per target. The corresponding covariance coefficients are used as extra features improving the classification accuracy. The proposed DF-SSmVEP is evaluated based on a real EEG dataset and the results corroborate its superiority. The proposed DF-SSmVEP outperforms its counterparts and achieved an average ITR of 30.7 ± 1.97 and an average accuracy of 92.5 ± 2.04, while the Radial Zoom and Rotation result in average ITRs of 18.35 ± 1 and 20.52 ± 2.5, and average accuracies of 68.12 ± 3.5 and 77.5 ± 3.5, respectively.},
}
@article {pmid35406445,
year = {2022},
author = {Asuthkar, S and Venkataraman, S and Avilala, J and Shishido, K and Vibhakar, R and Veo, B and Purvis, IJ and Guda, MR and Velpula, KK},
title = {SMYD3 Promotes Cell Cycle Progression by Inducing Cyclin D3 Transcription and Stabilizing the Cyclin D1 Protein in Medulloblastoma.},
journal = {Cancers},
volume = {14},
number = {7},
pages = {},
pmid = {35406445},
issn = {2072-6694},
abstract = {Medulloblastoma (MB) is the most common malignant pediatric brain tumor. Maximum safe resection, postoperative craniospinal irradiation, and chemotherapy are the standard of care for MB patients. MB is classified into four subgroups: Shh, Wnt, Group 3, and Group 4. Of these subgroups, patients with Myc+ Group 3 MB have the worst prognosis, necessitating alternative therapies. There is increasing interest in targeting epigenetic modifiers for treating pediatric cancers, including MB. Using an RNAi functional genomic screen, we identified the lysine methyltransferase SMYD3, as a crucial epigenetic regulator that drives the growth of Group 3 Myc+ MB cells. We demonstrated that SMYD3 directly binds to the cyclin D3 promoter to activate its transcription. Further, SMYD3 depletion significantly reduced MB cell proliferation and led to the downregulation of cyclin D3, cyclin D1, pRBSer795, with concomitant upregulations in RB in vitro. Similar results were obtained following pharmacological inhibition of SMYD3 using BCI-121 ex vivo. SMYD3 knockdown also promoted cyclin D1 ubiquitination, indicating that SMYD3 plays a vital role in stabilizing the cyclin D1 protein. Collectively, our studies demonstrate that SMYD3 drives cell cycle progression in Group 3 Myc+ MB cells and that targeting SMYD3 has the potential to improve clinical outcomes for high-risk patients.},
}
@article {pmid35405471,
year = {2022},
author = {Sun, B and Wu, Z and Hu, Y and Li, T},
title = {Golden subject is everyone: A subject transfer neural network for motor imagery-based brain computer interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {151},
number = {},
pages = {111-120},
doi = {10.1016/j.neunet.2022.03.025},
pmid = {35405471},
issn = {1879-2782},
abstract = {Electroencephalographic measurement of cortical activity subserving motor behavior varies among different individuals, restricting the potential of brain computer interfaces (BCIs) based on motor imagery (MI). How to deal with this variability and thereby improve the accuracy of BCI classification remains a key issue. This paper proposes a deep learning-based approach to transfer the data distribution from BCI-friendly - "golden subjects" to the data from more typical BCI-illiterate users. In this work, we use the perceptual loss to align the dimensionality-reduced BCI-illiterate data with the data of golden subjects in low dimensions, by which a subject transfer neural network (STNN) is proposed. The network consists of two parts: 1) a generator, which generates the transferred BCI-illiterate features, and 2) a CNN classifier, which is used for the classification of the transferred features, thus outperforming traditional classification methods both in terms of accuracy and robustness. Electroencephalography (EEG) signals from 25 healthy subjects performing MI of the right hand and foot were classified with an average accuracy of 88.2%±5.1%. The proposed model was further validated on the BCI Competition IV dataset 2b, and was demonstrated to be robust to inter-subject variations. The advantages of STNN allow it to bridge the gap between the golden subjects and the BCI-illiterate ones, paving the way to real-time BCI applications.},
}
@article {pmid35404821,
year = {2022},
author = {Wang, Q and Liu, F and Wan, G and Chen, Y},
title = {Inference of Brain States under Anesthesia with Meta Learning Based Deep Learning Models.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2022.3166517},
pmid = {35404821},
issn = {1558-0210},
abstract = {Monitoring the depth of unconsciousness during anesthesia is beneficial in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time. Different general anesthetics affect cerebral electrical activities in different ways. However, the performance of conventional machine learning models on EEG data is unsatisfactory due to the low Signal to Noise Ratio (SNR) in the EEG signals, especially in the office-based anesthesia EEG setting. Deep learning models have been used widely in the field of Brain Computer Interface (BCI) to perform classification and pattern recognition tasks due to their capability of good generalization and handling noises. Compared to other BCI applications, where deep learning has demonstrated encouraging results, the deep learning approach for classifying different brain consciousness states under anesthesia has been much less investigated. In this paper, we propose a new framework based on meta-learning using deep neural networks, named Anes-MetaNet, to classify brain states under anesthetics. The Anes-MetaNet is composed of Convolutional Neural Networks (CNN) to extract power spectrum features, and a time consequence model based on Long Short-Term Memory (LSTM) networks to capture the temporal dependencies, and a meta-learning framework to handle large cross-subject variability. We use a multi-stage training paradigm to improve the performance, which is justified by visualizing the high-level feature mapping. Experiments on the office-based anesthesia EEG dataset demonstrate the effectiveness of our proposed Anes-MetaNet by comparison of existing methods.},
}
@article {pmid35401871,
year = {2022},
author = {Xu, S and Zhu, L and Kong, W and Peng, Y and Hu, H and Cao, J},
title = {A novel classification method for EEG-based motor imagery with narrow band spatial filters and deep convolutional neural network.},
journal = {Cognitive neurodynamics},
volume = {16},
number = {2},
pages = {379-389},
pmid = {35401871},
issn = {1871-4080},
abstract = {The Common Spatial Pattern (CSP) algorithm is the most widely used method for decoding Electroencephalography (EEG) signals from motor imagery (MI) paradigm. However, due to the inter-subject variability, the CSP algorithm heavily relies on the selection of filter bands and extensive analytical processing time required to build an effective model, which has been a challenge in current research. In this paper, we propose a narrow filter bank CSP (NFBCSP) algorithm, which automatically determines the optimal narrow band for two-class motor imagery by band search tree, and a high-performance classification model dedicated to each subject can be obtained in a short time for online processing or further offline analysis. The optimal narrow band is combined with the CSP algorithm to extract the dynamic features in the EEG signals. For the multi-class motor imagery task, it is first transformed into multiple One-Versus-Rest (OVR) tasks and determines the corresponding optimal narrow bands. After extracting the features of each optimal narrow band separately, the Deep Convolutional Neural Network (DCNN) is used for the fusion of band features and classification of multi-class motor imagery. Finally, we verified our method using two different motor imagery datasets, the BCI-VR dataset with two classes of motor imagery and the BCI Competition IV dataset 2a with four classes of motor imagery. The experimental results show that the proposed method achieves an average classification accuracy of 86.43% for the two-class motor imagery task, and 76.87% for the four-class motor imagery task, which outperforms other recent methods.<br><br>Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-021-09721-x.},
}
@article {pmid35401092,
year = {2022},
author = {Choi, SI and Lee, JY and Lim, KM and Hwang, HJ},
title = {Evaluation of Real-Time Endogenous Brain-Computer Interface Developed Using Ear-Electroencephalography.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {842635},
pmid = {35401092},
issn = {1662-4548},
abstract = {While previous studies have demonstrated the feasibility of using ear-electroencephalography (ear-EEG) for the development of brain-computer interfaces (BCIs), most of them have been performed using exogenous paradigms in offline environments. To verify the reliable feasibility of constructing ear-EEG-based BCIs, the feasibility of using ear-EEG should be further demonstrated using another BCI paradigm, namely the endogenous paradigm, in real-time online environments. Exogenous and endogenous BCIs are to use the EEG evoked by external stimuli and induced by self-modulation, respectively. In this study, we investigated whether an endogenous ear-EEG-based BCI with reasonable performance can be implemented in online environments that mimic real-world scenarios. To this end, we used three different mental tasks, i.e., mental arithmetic, word association, and mental singing, and performed BCI experiments with fourteen subjects on three different days to investigate not only the reliability of a real-time endogenous ear-EEG-based BCI, but also its test-retest reliability. The mean online classification accuracy was almost 70%, which was equivalent to a marginal accuracy for a practical two-class BCI (70%), demonstrating the feasibility of using ear-EEG for the development of real-time endogenous BCIs, but further studies should follow to improve its performance enough to be used for practical ear-EEG-based BCI applications.},
}
@article {pmid35399917,
year = {2022},
author = {Pan, K and Li, L and Zhang, L and Li, S and Yang, Z and Guo, Y},
title = {A Noninvasive BCI System for 2D Cursor Control Using a Spectral-Temporal Long Short-Term Memory Network.},
journal = {Frontiers in computational neuroscience},
volume = {16},
number = {},
pages = {799019},
pmid = {35399917},
issn = {1662-5188},
abstract = {Two-dimensional cursor control is an important and challenging problem in the field of electroencephalography (EEG)-based brain computer interfaces (BCIs) applications. However, most BCIs based on categorical outputs are incapable of generating accurate and smooth control trajectories. In this article, a novel EEG decoding framework based on a spectral-temporal long short-term memory (stLSTM) network is proposed to generate control signals in the horizontal and vertical directions for accurate cursor control. Precisely, the spectral information is used to decode the subject's motor imagery intention, and the error-related P300 information is used to detect a deviation in the movement trajectory. The concatenated spectral and temporal features are fed into the stLSTM network and mapped to the velocities in vertical and horizontal directions of the 2D cursor under the velocity-constrained (VC) strategy, which enables the decoding network to fit the velocity in the imaginary direction and simultaneously suppress the velocity in the non-imaginary direction. This proposed framework was validated on a public real BCI control dataset. Results show that compared with the state-of-the-art method, the RMSE of the proposed method in the non-imaginary directions on the testing sets of 2D control tasks is reduced by an average of 63.45%. Besides, the visualization of the actual trajectories distribution of the cursor also demonstrates that the decoupling of velocity is capable of yielding accurate cursor control in complex path tracking tasks and significantly improves the control accuracy.},
}
@article {pmid35399915,
year = {2022},
author = {Branco, LRF and Ehteshami, A and Azgomi, HF and Faghih, RT},
title = {Closed-Loop Tracking and Regulation of Emotional Valence State From Facial Electromyogram Measurements.},
journal = {Frontiers in computational neuroscience},
volume = {16},
number = {},
pages = {747735},
pmid = {35399915},
issn = {1662-5188},
abstract = {Affective studies provide essential insights to address emotion recognition and tracking. In traditional open-loop structures, a lack of knowledge about the internal emotional state makes the system incapable of adjusting stimuli parameters and automatically responding to changes in the brain. To address this issue, we propose to use facial electromyogram measurements as biomarkers to infer the internal hidden brain state as feedback to close the loop. In this research, we develop a systematic way to track and control emotional valence, which codes emotions as being pleasant or obstructive. Hence, we conduct a simulation study by modeling and tracking the subject's emotional valence dynamics using state-space approaches. We employ Bayesian filtering to estimate the person-specific model parameters along with the hidden valence state, using continuous and binary features extracted from experimental electromyogram measurements. Moreover, we utilize a mixed-filter estimator to infer the secluded brain state in a real-time simulation environment. We close the loop with a fuzzy logic controller in two categories of regulation: inhibition and excitation. By designing a control action, we aim to automatically reflect any required adjustments within the simulation and reach the desired emotional state levels. Final results demonstrate that, by making use of physiological data, the proposed controller could effectively regulate the estimated valence state. Ultimately, we envision future outcomes of this research to support alternative forms of self-therapy by using wearable machine interface architectures capable of mitigating periods of pervasive emotions and maintaining daily well-being and welfare.},
}
@article {pmid35399355,
year = {2022},
author = {Müller-Putz, GR and Coyle, D and Lotte, F and Jin, J and Steyrl, D},
title = {Editorial: Long Term User Training and Preparation to Succeed in a Closed-Loop BCI Competition.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {869700},
pmid = {35399355},
issn = {1662-5161},
}
@article {pmid35398087,
year = {2022},
author = {Liu, C and Jin, J and Daly, I and Sun, H and Huang, Y and Wang, X and Cichocki, A},
title = {Bispectrum-based hybrid neural network for motor imagery classification.},
journal = {Journal of neuroscience methods},
volume = {375},
number = {},
pages = {109593},
doi = {10.1016/j.jneumeth.2022.109593},
pmid = {35398087},
issn = {1872-678X},
abstract = {BACKGROUND: The performance of motor imagery electroencephalogram (MI-EEG) decoding systems is easily affected by noise. As a higher-order spectra (HOS), the bispectrum is capable of suppressing Gaussian noise and increasing the signal-to-noise ratio of signals. However, the sum of logarithmic amplitudes (SLA) and the first order spectral moment (FOSM) features extracted from the bispectrum only use the numerical values of the bispectrum, ignoring the related information between different frequency bins.<br><br>NEW METHOD: In this study, we proposed a novel framework, termed a bispectrum-based hybrid neural network (BHNN), to make full use of bispectrum for improving the performance of the MI-based brain-computer interface (BCI). Specifically, the BHNN consisted of a convolutional neural network (CNN), gated recurrent units (GRU), and squeeze-and-excitation (SE) modules. The SE modules and CNNs are first used to learn the deep relation between frequency bins of the bispectrum estimated from different time window segmentations of the MI-EEG. Then, we used GRU to seek the overlooked sequential information of the bispectrum.<br><br>RESULTS: To validate the effectiveness of the proposed BHNN, three public BCI competition datasets were used in this study. The results demonstrated that the BHNN can achieve promising performance in decoding MI-EEG.<br><br>The statistical test results demonstrated that the proposed BHNN can significantly outperform other competing methods (p < =0.05).<br><br>CONCLUSION: The proposed BHNN is a novel bispectrum-based neural network, which can enhance the decoding performance of MI-based BCIs.},
}
@article {pmid35396257,
year = {2022},
author = {Lauridsen, K and Ly, A and Prévost, ED and McNulty, C and McGovern, DJ and Tay, JW and Dragavon, J and Root, DH},
title = {A Semi-Automated Workflow for Brain Slice Histology Alignment, Registration, and Cell Quantification (SHARCQ).},
journal = {eNeuro},
volume = {9},
number = {2},
pages = {},
pmid = {35396257},
issn = {2373-2822},
support = {F31 MH125569/MH/NIMH NIH HHS/United States ; R01 DA047443/DA/NIDA NIH HHS/United States ; S10 OD025072/OD/NIH HHS/United States ; },
mesh = {Animals ; *Brain/diagnostic imaging ; Brain Mapping/methods ; Histological Techniques ; *Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging ; Mice ; Workflow ; },
abstract = {Tools for refined cell-specific targeting have significantly contributed to understanding the characteristics and dynamics of distinct cellular populations by brain region. While advanced cell-labeling methods have accelerated the field of neuroscience, specifically in brain mapping, there remains a need to quantify and analyze the data. Here, by modifying a toolkit that localizes electrodes to brain regions (SHARP-Track; Slice Histology Alignment, Registration, and Probe-Track analysis), we introduce a post-imaging analysis tool to map histological images to established mouse brain atlases called SHARCQ (Slice Histology Alignment, Registration, and Cell Quantification). The program requires MATLAB, histological images, and either a manual or automatic cell count of the unprocessed images. SHARCQ simplifies the post-imaging analysis pipeline with a step-by-step GUI. We demonstrate that SHARCQ can be applied for a variety of mouse brain images, regardless of histology technique. In addition, SHARCQ rectifies discrepancies in mouse brain region borders between atlases by allowing the user to select between the Allen Brain Atlas or the digitized and modified Franklin-Paxinos Atlas for quantifying cell counts by region. SHARCQ produces quantitative and qualitative data, including counts of brain-wide region populations and a 3D model of registered cells within the atlas space. In summary, SHARCQ was designed as a neuroscience post-imaging analysis tool for cell-to-brain registration and quantification with a simple, accessible interface. All code is open-source and available for download (https://github.com/wildrootlab/SHARCQ).},
}
@article {pmid35395645,
year = {2022},
author = {Wu, X and Li, G and Jiang, S and Wellington, S and Liu, S and Wu, Z and Metcalfe, B and Chen, L and Zhang, D},
title = {Decoding continuous kinetic information of grasp from stereo-electroencephalographic (SEEG) recordings.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac65b1},
pmid = {35395645},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Hand Strength ; Humans ; Linear Models ; Neural Networks, Computer ; },
abstract = {Objective.Brain-computer interfaces (BCIs) have the potential to bypass damaged neural pathways and restore functionality lost due to injury or disease. Approaches to decoding kinematic information are well documented; however, the decoding of kinetic information has received less attention. Additionally, the possibility of using stereo-electroencephalography (SEEG) for kinetic decoding during hand grasping tasks is still largely unknown. Thus, the objective of this paper is to demonstrate kinetic parameter decoding using SEEG in patients performing a grasping task with two different force levels under two different ascending rates.Approach.Temporal-spectral representations were studied to investigate frequency modulation under different force tasks. Then, force amplitude was decoded from SEEG recordings using multiple decoders, including a linear model, a partial least squares model, an unscented Kalman filter, and three deep learning models (shallow convolutional neural network, deep convolutional neural network and the proposed CNN+RNN neural network).Main results.The current study showed that: (a) for some channel, both low-frequency modulation (event-related desynchronization (ERD)) and high-frequency modulation (event-related synchronization) were sustained during prolonged force holding periods; (b) continuously changing grasp force can be decoded from the SEEG signals; (c) the novel CNN+RNN deep learning model achieved the best decoding performance, with the predicted force magnitude closely aligned to the ground truth under different force amplitudes and changing rates.Significance.This work verified the possibility of decoding continuously changing grasp force using SEEG recordings. The result presented in this study demonstrated the potential of SEEG recordings for future BCI application.},
}
@article {pmid35390282,
year = {2022},
author = {Aflalo, T and Zhang, C and Revechkis, B and Rosario, E and Pouratian, N and Andersen, RA},
title = {Implicit mechanisms of intention.},
journal = {Current biology : CB},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cub.2022.03.047},
pmid = {35390282},
issn = {1879-0445},
abstract = {High-level cortical regions encode motor decisions before or even absent awareness, suggesting that neural processes predetermine behavior before conscious choice. Such early neural encoding challenges popular conceptions of human agency. It also raises fundamental questions for brain-machine interfaces (BMIs) that traditionally assume that neural activity reflects the user's conscious intentions. Here, we study the timing of human posterior parietal cortex single-neuron activity recorded from implanted microelectrode arrays relative to the explicit urge to initiate movement. Participants were free to choose when to move, whether to move, and what to move, and they retrospectively reported the time they felt the urge to move. We replicate prior studies by showing that posterior parietal cortex (PPC) neural activity sharply rises hundreds of milliseconds before the reported urge. However, we find that this "preconscious" activity is part of a dynamic neural population response that initiates much earlier, when the participant first chooses to perform the task. Together with details of neural timing, our results suggest that PPC encodes an internal model of the motor planning network that transforms high-level task objectives into appropriate motor behavior. These new data challenge traditional interpretations of early neural activity and offer a more holistic perspective on the interplay between choice, behavior, and their neural underpinnings. Our results have important implications for translating BMIs into more complex real-world environments. We find that early neural dynamics are sufficient to drive BMI movements before the participant intends to initiate movement. Appropriate algorithms ensure that BMI movements align with the subject's awareness of choice.},
}
@article {pmid35387250,
year = {2022},
author = {Kalashami, MP and Pedram, MM and Sadr, H},
title = {EEG Feature Extraction and Data Augmentation in Emotion Recognition.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {7028517},
pmid = {35387250},
issn = {1687-5273},
mesh = {Arousal ; *Electroencephalography/methods ; Emotions ; *Neural Networks, Computer ; Support Vector Machine ; },
abstract = {Emotion recognition is a challenging problem in Brain-Computer Interaction (BCI). Electroencephalogram (EEG) gives unique information about brain activities that are created due to emotional stimuli. This is one of the most substantial advantages of brain signals in comparison to facial expression, tone of voice, or speech in emotion recognition tasks. However, the lack of EEG data and high dimensional EEG recordings lead to difficulties in building effective classifiers with high accuracy. In this study, data augmentation and feature extraction techniques are proposed to solve the lack of data problem and high dimensionality of data, respectively. In this study, the proposed method is based on deep generative models and a data augmentation strategy called Conditional Wasserstein GAN (CWGAN), which is applied to the extracted features to regenerate additional EEG features. DEAP dataset is used to evaluate the effectiveness of the proposed method. Finally, a standard support vector machine and a deep neural network with different tunes were implemented to build effective models. Experimental results show that using the additional augmented data enhances the performance of EEG-based emotion recognition models. Furthermore, the mean accuracy of classification after data augmentation is increased 6.5% for valence and 3.0% for arousal, respectively.},
}
@article {pmid35386387,
year = {2022},
author = {Reece, AS and Hulse, GK},
title = {Geospatiotemporal and causal inference study of cannabis and other drugs as risk factors for female breast cancer USA 2003-2017.},
journal = {Environmental epigenetics},
volume = {8},
number = {1},
pages = {dvac006},
pmid = {35386387},
issn = {2058-5888},
abstract = {Breast cancer (BC) is the commonest human cancer and its incidence (BC incidence, BCI) is rising worldwide. Whilst both tobacco and alcohol have been linked to BCI genotoxic cannabinoids have not been investigated. Age-adjusted state-based BCI 2003-2017 was taken from the Surveillance Epidemiology and End Results database of the Centers for Disease Control. Drug use from the National Survey of Drug Use and Health, response rate 74.1%. Median age, median household income and ethnicity were from US census. Inverse probability weighted (ipw) multivariable regression conducted in R. In bivariate analysis BCI was shown to be significantly linked with rising cannabis exposure {β-est. = 3.93 [95% confidence interval 2.99, 4.87], P = 1.10 × 10-15}
. At 8 years lag cigarettes:cannabis [β-est. = 2660 (2150.4, 3169.3), P = 4.60 × 10-22] and cannabis:alcoholism [β-est. = 7010 (5461.6, 8558.4), P = 1.80 × 10-17] were significant in ipw-panel regression. Terms including cannabidiol [CBD; β-est. = 16.16 (0.39, 31.93), P = 0.446] and cannabigerol [CBG; β-est. = 6.23 (2.06, 10.39), P = 0.0034] were significant in spatiotemporal models lagged 1:2 years, respectively. Cannabis-liberal paradigms had higher BCI [67.50 ± 0.26 v. 65.19 ± 0.21/100 000 (mean ± SEM), P = 1.87 × 10-11; β-est. = 2.31 (1.65, 2.96), P = 9.09 × 10-12]. 55/58 expected values >1.25 and 13/58 >100. Abortion was independently and causally significant in space-time models. Data show that exposure to cannabis and the cannabinoids Δ9-tetrahydrocannabinol, CBD, CBG and alcoholism fulfil quantitative causal criteria for BCI across space and time. Findings are robust to adjustment for age and several known sociodemographic, socio-economic and hormonal risk factors and establish cannabinoids as an additional risk factor class for breast carcinogenesis. BCI is higher under cannabis-liberal legal paradigms.},
}
@article {pmid35386356,
year = {2022},
author = {Yang, B and Liang, C and Chen, D and Cheng, F and Zhang, Y and Wang, S and Shu, J and Huang, X and Wang, J and Xia, K and Ying, L and Shi, K and Wang, C and Wang, X and Li, F and Zhao, Q and Chen, Q},
title = {A conductive supramolecular hydrogel creates ideal endogenous niches to promote spinal cord injury repair.},
journal = {Bioactive materials},
volume = {15},
number = {},
pages = {103-119},
pmid = {35386356},
issn = {2452-199X},
abstract = {The current effective method for treatment of spinal cord injury (SCI) is to reconstruct the biological microenvironment by filling the injured cavity area and increasing neuronal differentiation of neural stem cells (NSCs) to repair SCI. However, the method is characterized by several challenges including irregular wounds, and mechanical and electrical mismatch of the material-tissue interface. In the current study, a unique and facile agarose/gelatin/polypyrrole (Aga/Gel/PPy, AGP3) hydrogel with similar conductivity and modulus as the spinal cord was developed by altering the concentration of Aga and PPy. The gelation occurred through non-covalent interactions, and the physically crosslinked features made the AGP3 hydrogels injectable. In vitro cultures showed that AGP3 hydrogel exhibited excellent biocompatibility, and promoted differentiation of NSCs toward neurons whereas it inhibited over-proliferation of astrocytes. The in vivo implanted AGP3 hydrogel completely covered the tissue defects and reduced injured cavity areas. In vivo studies further showed that the AGP3 hydrogel provided a biocompatible microenvironment for promoting endogenous neurogenesis rather than glial fibrosis formation, resulting in significant functional recovery. RNA sequencing analysis further indicated that AGP3 hydrogel significantly modulated expression of neurogenesis-related genes through intracellular Ca2+ signaling cascades. Overall, this supramolecular strategy produces AGP3 hydrogel that can be used as favorable biomaterials for SCI repair by filling the cavity and imitating the physiological properties of the spinal cord.},
}
@article {pmid35384458,
year = {2022},
author = {Tzanis, E and Damilakis, J},
title = {A novel methodology to train and deploy a machine learning model for personalized dose assessment in head CT.},
journal = {European radiology},
volume = {},
number = {},
pages = {},
pmid = {35384458},
issn = {1432-1084},
abstract = {OBJECTIVES: To propose a machine learning-based methodology for the creation of radiation dose maps and the prediction of patient-specific organ/tissue doses associated with head CT examinations.<br><br>METHODS: CT data were collected retrospectively for 343 patients who underwent standard head CT examinations. Patient-specific Monte Carlo (MC) simulations were performed to determine the radiation dose distribution to patients' organs/tissues. The collected CT images and the MC-produced dose maps were processed and used for the training of the deep neural network (DNN) model. For the training and validation processes, data from 231 and 112 head CT examinations, respectively, were used. Furthermore, a software tool was developed to produce dose maps from head CT images using the trained DNN model and to automatically calculate the dose to the brain and cranial bones.<br><br>RESULTS: The mean (range) percentage differences between the doses predicted from the DNN model and those provided by MC simulations for the brain, eye lenses, and cranial bones were 4.5% (0-17.7%), 5.7% (0.2-19.0%), and 5.2% (0.1-18.9%), respectively. The graphical user interface of the software offers a user-friendly way for radiation dose/risk assessment. The implementation of the DNN allowed for a 97% reduction in the computational time needed for the dose estimations.<br><br>CONCLUSIONS: A novel methodology that allows users to develop a DNN model for patient-specific CT dose prediction was developed and implemented. The approach demonstrated herein allows accurate and fast radiation dose estimation for the brain, eye lenses, and cranial bones of patients who undergo head CT examinations and can be used in everyday clinical practice.<br><br>KEY POINTS: • The methodology presented herein allows fast and accurate radiation dose estimation for the brain, eye lenses, and cranial bones of patients who undergo head CT examinations and can be implemented in everyday clinical practice. • The scripts developed in the current study will allow users to train models for the acquisition protocols of their CT scanners, generate dose maps, estimate the doses to the brain and cranial bones, and estimate the lifetime attributable risk of radiation-induced brain cancer.},
}
@article {pmid35382585,
year = {2022},
author = {Lamarre, GPA and Pardikes, NA and Segar, S and Hackforth, CN and Laguerre, M and Vincent, B and Lopez, Y and Perez, F and Bobadilla, R and Silva, JAR and Basset, Y},
title = {More winners than losers over 12 years of monitoring tiger moths (Erebidae: Arctiinae) on Barro Colorado Island, Panama.},
journal = {Biology letters},
volume = {18},
number = {4},
pages = {20210519},
pmid = {35382585},
issn = {1744-957X},
mesh = {Animals ; Climate Change ; Colorado ; Ecology ; *Moths/physiology ; Trees ; *Tropical Climate ; },
abstract = {Understanding the causes and consequences of insect declines has become an important goal in ecology, particularly in the tropics, where most terrestrial diversity exists. Over the past 12 years, the ForestGEO Arthropod Initiative has systematically monitored multiple insect groups on Barro Colorado Island (BCI), Panama, providing baseline data for assessing long-term population trends. Here, we estimate the rates of change in abundance among 96 tiger moth species on BCI. Population trends of most species were stable (n = 20) or increasing (n = 62), with few (n = 14) declining species. Our analysis of morphological and climatic sensitivity traits associated with population trends shows that species-specific responses to climate were most strongly linked with trends. Specifically, tiger moth species that are more abundant in warmer and wetter years are more likely to show population increases. Our study contrasts with recent findings indicating insect decline in tropical and temperate regions. These results highlight the significant role of biotic responses to climate in determining long-term population trends and suggest that future climate changes are likely to impact tropical insect communities.},
}
@article {pmid35382584,
year = {2022},
author = {Bujan, J and Nottingham, AT and Velasquez, E and Meir, P and Kaspari, M and Yanoviak, SP},
title = {Tropical ant community responses to experimental soil warming.},
journal = {Biology letters},
volume = {18},
number = {4},
pages = {20210518},
pmid = {35382584},
issn = {1744-957X},
mesh = {Animals ; *Ants/physiology ; Climate Change ; Global Warming ; Soil ; *Thermotolerance ; },
abstract = {Climate change is one of the primary agents of the global decline in insect abundance. Because of their narrow thermal ranges, tropical ectotherms are predicted to be most threatened by global warming, yet tests of this prediction are often confounded by other anthropogenic disturbances. We used a tropical forest soil warming experiment to directly test the effect of temperature increase on litter-dwelling ants. Two years of continuous warming led to a change in ant community between warming and control plots. Specifically, six ant genera were recorded only on warming plots, and one genus only on control plots. Wasmannia auropuctata, a species often invasive elsewhere but native to this forest, was more abundant in warmed plots. Ant recruitment at baits was best predicted by soil surface temperature and ant heat tolerance. These results suggest that heat tolerance is useful for predicting changes in daily foraging activity, which is directly tied to colony fitness. We show that a 2-year increase in temperature (of 2-4°C) can have a profound effect on the most abundant insects, potentially favouring species with invasive traits and moderate heat tolerances.},
}
@article {pmid35382179,
year = {2022},
author = {Fokin, AA and Wycech Knight, J and Yoshinaga, K and Abid, AT and Grady, R and Alayon, AL and Puente, I},
title = {Blunt Cardiac Injury in Patients With Sternal Fractures.},
journal = {Cureus},
volume = {14},
number = {3},
pages = {e22841},
pmid = {35382179},
issn = {2168-8184},
abstract = {Background Blunt cardiac injury (BCI) is a possible consequence of sternal fractures (SF). There is a scarcity of studies addressing BCI in patients with different types of SF and with pre-existing cardiac conditions. The goal of this study was to delineate diagnostic patterns of BCI in different cohorts of SF patients. Methods This retrospective cohort study included 380 blunt trauma patients admitted to two level 1 trauma centers between January 2015 and March 2020 with radiologically confirmed SF. Electrocardiography, cardiac enzymes and echocardiography were evaluated for BCI diagnosis. Analyzed variables included: age, comorbidities, injury severity score, Glasgow coma score, type of SF (isolated, combined, displaced), incidence of traumatic brain injury, co-injuries, retrosternal hematoma, intensive care unit admissions, hospital lengths of stay, and mortality. Results In 380 SF patients there were 250 (66%) females and 130 (34%) males and the mean age was 63 years old. Electrocardiography was done in all patients, cardiac enzymes in 234 (62%) and echocardiography in 181 (48%). BCI was diagnosed in 19 (5%) of patients, all having combined SF. BCI patients had higher injury severity score (mean 18.4) and 14 (74%) had pulmonary co-injuries. Multivariable analysis confirmed pulmonary co-injuries as a statistically significant predictor of BCI (p<0.001). BCI patients compared to no BCI patients had all three tests (electrocardiography, cardiac enzymes and echocardiography) performed statistically more often (90% vs 36%, p<0.001). SF patients with pre-injury cardiac comorbidities had similar incidence of BCI as without cardiac comorbidities (5% vs 6%, p=0.6). In SF patients with traumatic brain injury, cardiac enzymes (troponin, creatine kinase) were elevated significantly more often compared to patients without traumatic brain injury (58% vs 38%, p=0.02). SF displacement or retrosternal hematoma presence were not associated with BCI. Mortality in SF patients with BCI versus without was not statistically different (16 vs 9%, p=0.4). Conclusions Blunt cardiac injury is rare in patients with SF. Higher degree of BCI suspicion must be applied in combined SF patients, especially those with pulmonary co-injuries. Cardiac comorbidities did not affect the rate of BCI. Echocardiography for BCI diagnosis is essential in SF patients with traumatic brain injury, as cardiac enzymes may be less informative, however is less important in isolated SF patients. Performing all three diagnostic tests in combined SF patients improves the accuracy of BCI diagnosis.},
}
@article {pmid35381455,
year = {2022},
author = {Khan, A and Rasool, S},
title = {Game induced emotion analysis using electroencephalography.},
journal = {Computers in biology and medicine},
volume = {145},
number = {},
pages = {105441},
doi = {10.1016/j.compbiomed.2022.105441},
pmid = {35381455},
issn = {1879-0534},
abstract = {Organizations vie to develop insights into the psychological aspects of consumer decision-making to enhance their products accordingly. Understanding how emotions and personality traits influence the choices we make is an integral part of product design. In this paper, we have employed machine learning algorithms to profile discrete emotions, in response to video games stimuli, based on features extracted from recorded electroencephalography (EEG) and to understand certain characteristics of personality. Four video games from different genres have been used for emotion elicitation and players' EEG signals are recorded. EEG being a non-stationary, non-linear and extremely noisy signal has been cleaned using a Savitzky-Golay filter which is found to be suitable for single-channel EEG devices. Seven out of sixteen features from time, frequency and time-frequency domains have been selected using Random Forest and used to classify emotions. Support Vector Machine, k-Nearest Neighbour and Gradient Boosted Trees classifiers have been used where the highest classification accuracy 82.26% is achieved with Boosted Trees classifier. Our findings propagate that for a single-channel EEG device, only four discrete emotions (happy, bored, relaxed, stressed) can be classified where two emotions happy and bored achieved the highest individual accuracy of 88.89% and 85.29% respectively with the Gradient Boosted Trees Classifier. In this study, we have also identified personality traits, extroversion and neuroticism influence players' perception of video games. The results indicate that players with low extroversion prefer relatively slow and strategy games as compared to highly extroverted. It has also been identified that puzzle and racing games are well-liked irrespective of the levels of the two personality traits.},
}
@article {pmid35379947,
year = {2022},
author = {Pereira, TD and Tabris, N and Matsliah, A and Turner, DM and Li, J and Ravindranath, S and Papadoyannis, ES and Normand, E and Deutsch, DS and Wang, ZY and McKenzie-Smith, GC and Mitelut, CC and Castro, MD and D'Uva, J and Kislin, M and Sanes, DH and Kocher, SD and Wang, SS and Falkner, AL and Shaevitz, JW and Murthy, M},
title = {SLEAP: A deep learning system for multi-animal pose tracking.},
journal = {Nature methods},
volume = {19},
number = {4},
pages = {486-495},
pmid = {35379947},
issn = {1548-7105},
support = {R35 NS111580-02//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; R01 NS104899/NS/NINDS NIH HHS/United States ; GM137424-01//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; R01 DC011284/DC/NIDCD NIH HHS/United States ; DGE-1148900//National Science Foundation (NSF)/ ; R35 NS111580/NS/NINDS NIH HHS/United States ; R00 MH109674/MH/NIMH NIH HHS/United States ; PHY-1734030//National Science Foundation (NSF)/ ; },
mesh = {Algorithms ; Animals ; Behavior, Animal ; *Deep Learning ; Head ; Machine Learning ; Mice ; Social Behavior ; },
abstract = {The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation in tools to quantify natural animal behavior. While advances in deep learning and computer vision have enabled markerless pose estimation in individual animals, extending these to multiple animals presents unique challenges for studies of social behaviors or animals in their natural environments. Here we present Social LEAP Estimates Animal Poses (SLEAP), a machine learning system for multi-animal pose tracking. This system enables versatile workflows for data labeling, model training and inference on previously unseen data. SLEAP features an accessible graphical user interface, a standardized data model, a reproducible configuration system, over 30 model architectures, two approaches to part grouping and two approaches to identity tracking. We applied SLEAP to seven datasets across flies, bees, mice and gerbils to systematically evaluate each approach and architecture, and we compare it with other existing approaches. SLEAP achieves greater accuracy and speeds of more than 800 frames per second, with latencies of less than 3.5 ms at full 1,024 × 1,024 image resolution. This makes SLEAP usable for real-time applications, which we demonstrate by controlling the behavior of one animal on the basis of the tracking and detection of social interactions with another animal.},
}
@article {pmid35378515,
year = {2022},
author = {Sombeck, JT and Heye, J and Kumaravelu, K and Goetz, SM and Peterchev, AV and Grill, WM and Bensmaia, S and Miller, LE},
title = {Characterizing the short-latency evoked response to intracortical microstimulation across a multi-electrode array.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac63e8},
pmid = {35378515},
issn = {1741-2552},
support = {F31 NS115478/NS/NINDS NIH HHS/United States ; R01 NS095251/NS/NINDS NIH HHS/United States ; T32 HD007418/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; Electric Stimulation/methods ; Electrodes, Implanted ; Macaca mulatta ; Microelectrodes ; *Somatosensory Cortex/physiology ; },
abstract = {Objective.Persons with tetraplegia can use brain-machine interfaces to make visually guided reaches with robotic arms. Without somatosensory feedback, these movements will likely be slow and imprecise, like those of persons who retain movement but have lost proprioception. Intracortical microstimulation (ICMS) has promise for providing artificial somatosensory feedback. ICMS that mimics naturally occurring neural activity, may allow afferent interfaces that are more informative and easier to learn than stimulation evoking unnaturalistic activity. To develop such biomimetic stimulation patterns, it is important to characterize the responses of neurons to ICMS.Approach.Using a Utah multi-electrode array, we recorded activity evoked by both single pulses and trains of ICMS at a wide range of amplitudes and frequencies in two rhesus macaques. As the electrical artifact caused by ICMS typically prevents recording for many milliseconds, we deployed a custom rapid-recovery amplifier with nonlinear gain to limit signal saturation on the stimulated electrode. Across all electrodes after stimulation, we removed the remaining slow return to baseline with acausal high-pass filtering of time-reversed recordings.Main results.After single pulses of stimulation, we recorded what was likely transsynaptically-evoked activity even on the stimulated electrode as early as ∼0.7 ms. This was immediately followed by suppressed neural activity lasting 10-150 ms. After trains, this long-lasting inhibition was replaced by increased firing rates for ∼100 ms. During long trains, the evoked response on the stimulated electrode decayed rapidly while the response was maintained on non-stimulated channels.Significance.The detailed description of the spatial and temporal response to ICMS can be used to better interpret results from experiments that probe circuit connectivity or function of cortical areas. These results can also contribute to the design of stimulation patterns to improve afferent interfaces for artificial sensory feedback.},
}
@article {pmid35378341,
year = {2022},
author = {Kumari, R and Janković, MM and Costa, A and Savić, AM and Konstantinović, L and Djordjević, O and Vucković, A},
title = {Short term priming effect of brain-actuated muscle stimulation using bimanual movements in stroke.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {138},
number = {},
pages = {108-121},
doi = {10.1016/j.clinph.2022.03.002},
pmid = {35378341},
issn = {1872-8952},
abstract = {OBJECTIVE: Brain-computer interface triggered-functional electrical stimulation (BCI-FES) is an emerging neurorehabilitation therapy post stroke, mostly for the affected hand. We explored the feasibility of a bimanual BCI-FES and its short-term priming effects, i.e. stimuli-induced behaviour change. We compared EEG parameters between unimanual and bimanual movements and differentiated the effect of age from the effect of stroke.<br><br>METHODS: Ten participants with subacute stroke, ten age-matched older healthy adults, and ten younger healthy adults underwent unimanual and bimanual BCI-FES sessions. Delta alpha ratio (DAR) and brain symmetry index (BSI) were derived from the pre- and post- resting-state EEG. Event-related desynchronization (ERD) and laterality index were derived from movement- EEG.<br><br>RESULTS: Participants were able to control bimanual BCI-FES. ERD was predominantly contralateral for unimanual movements and bilateral for bimanual movements. DAR and BSI only changed in healthy controls. Baseline values indicated that DAR was affected by stroke while BSI was affected by both age and stroke.<br><br>CONCLUSIONS: Bimanual BCI control offers a larger repertoire of movements, while causing the same short-term changes as unimanual BCI-FES. Prolonged practice may be required to achieve a measurable effect on DAR and BSI for stroke.<br><br>SIGNIFICANCE: Bimanual BCI-FES is feasible in people affected by stroke.},
}
@article {pmid35377345,
year = {2022},
author = {Dannhauer, M and Huang, Z and Beynel, L and Wood, E and Bukhari-Parlakturk, N and Peterchev, AV},
title = {TAP: targeting and analysis pipeline for optimization and verification of coil placement in transcranial magnetic stimulation.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac63a4},
pmid = {35377345},
issn = {1741-2552},
support = {KL2 TR002554/TR/NCATS NIH HHS/United States ; RF1 MH114253/MH/NIMH NIH HHS/United States ; RF1 MH114268/MH/NIMH NIH HHS/United States ; U01 AG050618/AG/NIA NIH HHS/United States ; },
mesh = {Brain/physiology ; Humans ; *Neuronavigation/methods ; Prospective Studies ; Retrospective Studies ; *Transcranial Magnetic Stimulation/methods ; },
abstract = {Objective.Transcranial magnetic stimulation (TMS) can modulate brain function via an electric field (E-field) induced in a brain region of interest (ROI). The ROI E-field can be computationally maximized and set to match a specific reference using individualized head models to find the optimal coil placement and stimulus intensity. However, the available software lacks many practical features for prospective planning of TMS interventions and retrospective evaluation of the experimental targeting accuracy.Approach.The TMS targeting and analysis pipeline (TAP) software uses an MRI/fMRI-derived brain target to optimize coil placement considering experimental parameters such as the subject's hair thickness and coil placement restrictions. The coil placement optimization is implemented in SimNIBS 3.2, for which an additional graphical user interface (TargetingNavigator) is provided to visualize/adjust procedural parameters. The coil optimization process also computes the E-field at the target, allowing the selection of the TMS device intensity setting to achieve specific E-field strengths. The optimized coil placement information is prepared for neuronavigation software, which supports targeting during the TMS procedure. The neuronavigation system can record the coil placement during the experiment, and these data can be processed in TAP to quantify the accuracy of the experimental TMS coil placement and induced E-field.Main results.TAP was demonstrated in a study consisting of three repetitive TMS sessions in five subjects. TMS was delivered by an experienced operator under neuronavigation with the computationally optimized coil placement. Analysis of the experimental accuracy from the recorded neuronavigation data indicated coil location and orientation deviations up to about 2 mm and 2°, respectively, resulting in an 8% median decrease in the target E-field magnitude compared to the optimal placement.Significance.TAP supports navigated TMS with a variety of features for rigorous and reproducible stimulation delivery, including planning and evaluation of coil placement and intensity selection for E-field-based dosing.},
}
@article {pmid35372429,
year = {2022},
author = {Huang, X and Jin, K and Zhu, J and Xue, Y and Si, K and Zhang, C and Meng, S and Gong, W and Ye, J},
title = {A Structure-Related Fine-Grained Deep Learning System With Diversity Data for Universal Glaucoma Visual Field Grading.},
journal = {Frontiers in medicine},
volume = {9},
number = {},
pages = {832920},
pmid = {35372429},
issn = {2296-858X},
abstract = {Purpose: Glaucoma is the main cause of irreversible blindness worldwide. However, the diagnosis and treatment of glaucoma remain difficult because of the lack of an effective glaucoma grading measure. In this study, we aimed to propose an artificial intelligence system to provide adequate assessment of glaucoma patients.<br><br>Methods: A total of 16,356 visual fields (VFs) measured by Octopus perimeters and Humphrey Field Analyzer (HFA) were collected, from three hospitals in China and the public Harvard database. We developed a fine-grained grading deep learning system, named FGGDL, to evaluate the VF loss, compared to ophthalmologists. Subsequently, we discuss the relationship between structural and functional damage for the comprehensive evaluation of glaucoma level. In addition, we developed an interactive interface and performed a cross-validation study to test its auxiliary ability. The performance was valued by F1 score, overall accuracy and area under the curve (AUC).<br><br>Results: The FGGDL achieved a high accuracy of 85 and 90%, and AUC of 0.93 and 0.90 for HFA and Octopus data, respectively. It was significantly superior (p < 0.01) to that of medical students and nearly equal (p = 0.614) to that of ophthalmic clinicians. For the cross-validation study, the diagnosis accuracy was almost improved (p < 0.05).<br><br>Conclusion: We proposed a deep learning system to grade VF of glaucoma with a high detection accuracy, for effective and adequate assessment for glaucoma patients. Besides, with the convenient and credible interface, this system can promote telemedicine and be used as a self-assessment tool for patients with long-duration diseases.},
}
@article {pmid35371255,
year = {2022},
author = {Zeng, C and Mu, Z and Wang, Q},
title = {Classifying Driving Fatigue by Using EEG Signals.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {1885677},
pmid = {35371255},
issn = {1687-5273},
mesh = {Accidents, Traffic/prevention &amp; control ; *Automobile Driving ; Brain ; *Electroencephalography/methods ; Fatigue ; Humans ; },
abstract = {Fatigue driving is one of the main reasons for the occurrence of traffic accidents. Brain-computer interface, as a human-computer interaction method based on EEG signals, can communicate with the outside world and move freely through brain signals without relying on the peripheral neuromuscular system. In this paper, a simulation driving platform composed of driving simulation equipment and driving simulation software is used to simulate the real driving process. The EEG signals of the subjects are collected through simulated driving, and the EEG of five subjects is selected as the training sample, and the remaining one is the subject. As a test sample, perform feature extraction and classification experiments, select any set of normal signals and fatigue signals recorded in the driving fatigue experiment for data analysis, and then study the classification of driver fatigue levels. Experiments have proved that the PSO-H-ELM algorithm has only about 4% advantage compared with the average accuracy of the KNN algorithm and the SVM algorithm. The gap is not as big as expected, but as a new algorithm, it is applied to the detection of fatigue EEG. The two traditional algorithms are indeed more suitable. It shows that the driver fatigue level can be judged by detecting EEG, which will provide a basis for the development of on-board, real-time driving fatigue alarm devices. It will lay the foundation for traffic management departments to intervene in driving fatigue reasonably and provide a reliable basis for minimizing traffic accidents.},
}
@article {pmid35370596,
year = {2022},
author = {Peng, F and Li, M and Zhao, SN and Xu, Q and Xu, J and Wu, H},
title = {Control of a Robotic Arm With an Optimized Common Template-Based CCA Method for SSVEP-Based BCI.},
journal = {Frontiers in neurorobotics},
volume = {16},
number = {},
pages = {855825},
pmid = {35370596},
issn = {1662-5218},
abstract = {Recently, the robotic arm control system based on a brain-computer interface (BCI) has been employed to help the disabilities to improve their interaction abilities without body movement. However, it's the main challenge to implement the desired task by a robotic arm in a three-dimensional (3D) space because of the instability of electroencephalogram (EEG) signals and the interference by the spontaneous EEG activities. Moreover, the free motion control of a manipulator in 3D space is a complicated operation that requires more output commands and higher accuracy for brain activity recognition. Based on the above, a steady-state visual evoked potential (SSVEP)-based synchronous BCI system with six stimulus targets was designed to realize the motion control function of the seven degrees of freedom (7-DOF) robotic arm. Meanwhile, a novel template-based method, which builds the optimized common templates (OCTs) from various subjects and learns spatial filters from the common templates and the multichannel EEG signal, was applied to enhance the SSVEP recognition accuracy, called OCT-based canonical correlation analysis (OCT-CCA). The comparison results of offline experimental based on a public benchmark dataset indicated that the proposed OCT-CCA method achieved significant improvement of detection accuracy in contrast to CCA and individual template-based CCA (IT-CCA), especially using a short data length. In the end, online experiments with five healthy subjects were implemented for achieving the manipulator real-time control system. The results showed that all five subjects can accomplish the tasks of controlling the manipulator to reach the designated position in the 3D space independently.},
}
@article {pmid35370578,
year = {2022},
author = {Gao, D and Zheng, W and Wang, M and Wang, L and Xiao, Y and Zhang, Y},
title = {A Zero-Padding Frequency Domain Convolutional Neural Network for SSVEP Classification.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {815163},
pmid = {35370578},
issn = {1662-5161},
abstract = {The brain-computer interface (BCI) of steady-state visual evoked potential (SSVEP) is one of the fundamental ways of human-computer communication. The main challenge is that there may be a nonlinear relationship between different SSVEP in other states. For improving the performance of SSVEP BCI, a novel CNN algorithm model is proposed in this study. Based on the discrete Fourier transform to calculate the signal's power spectral density (PSD), we perform zero-padding in the signal's time domain to improve its performance on the PSD and make it more refined. In this way, the frequency point interval in the PSD of the SSVEP is consistent with the minimum gap between the stimulation frequency. Combining the nonlinear transformation capabilities of CNN in deep learning, a zero-padding frequency domain convolutional neural network (ZPFDCNN) model is proposed. Extensive experiments based on the SSVEP dataset validate the effectiveness of our method. The study verifies that the proposed ZPFDCNN method can improve the effectiveness of the SSVEP-based high-speed BCI ITR. It has massive potential in the application of BCI.},
}
@article {pmid35368960,
year = {2022},
author = {Malibari, AA and Al-Wesabi, FN and Obayya, M and Alkhonaini, MA and Hamza, MA and Motwakel, A and Yaseen, I and Zamani, AS},
title = {Arithmetic Optimization with RetinaNet Model for Motor Imagery Classification on Brain Computer Interface.},
journal = {Journal of healthcare engineering},
volume = {2022},
number = {},
pages = {3987494},
pmid = {35368960},
issn = {2040-2309},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {Brain Computer Interface (BCI) technology commonly used to enable communication for the person with movement disability. It allows the person to communicate and control assistive robots by the use of electroencephalogram (EEG) or other brain signals. Though several approaches have been available in the literature for learning EEG signal feature, the deep learning (DL) models need to further explore for generating novel representation of EEG features and accomplish enhanced outcomes for MI classification. With this motivation, this study designs an arithmetic optimization with RetinaNet based deep learning model for MI classification (AORNDL-MIC) technique on BCIs. The proposed AORNDL-MIC technique initially exploits Multiscale Principal Component Analysis (MSPCA) approach for the EEG signal denoising and Continuous Wavelet Transform (CWT) is exploited for the transformation of 1D-EEG signal into 2D time-frequency amplitude representation, which enables to utilize the DL model via transfer learning approach. In addition, the DL based RetinaNet is applied for extracting of feature vectors from the EEG signal which are then classified with the help of ID3 classifier. In order to optimize the classification efficiency of the AORNDL-MIC technique, arithmetical optimization algorithm (AOA) is employed for hyperparameter tuning of the RetinaNet. The experimental analysis of the AORNDL-MIC algorithm on the benchmark data sets reported its promising performance over the recent state of art methodologies.},
}
@article {pmid35368726,
year = {2021},
author = {Chang, H and Zong, Y and Zheng, W and Tang, C and Zhu, J and Li, X},
title = {Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network.},
journal = {Frontiers in psychiatry},
volume = {12},
number = {},
pages = {837149},
pmid = {35368726},
issn = {1664-0640},
abstract = {The main characteristic of depression is emotional dysfunction, manifested by increased levels of negative emotions and decreased levels of positive emotions. Therefore, accurate emotion recognition is an effective way to assess depression. Among the various signals used for emotion recognition, electroencephalogram (EEG) signal has attracted widespread attention due to its multiple advantages, such as rich spatiotemporal information in multi-channel EEG signals. First, we use filtering and Euclidean alignment for data preprocessing. In the feature extraction, we use short-time Fourier transform and Hilbert-Huang transform to extract time-frequency features, and convolutional neural networks to extract spatial features. Finally, bi-directional long short-term memory explored the timing relationship. Before performing the convolution operation, according to the unique topology of the EEG channel, the EEG features are converted into 3D tensors. This study has achieved good results on two emotion databases: SEED and Emotional BCI of 2020 WORLD ROBOT COMPETITION. We applied this method to the recognition of depression based on EEG and achieved a recognition rate of more than 70% under the five-fold cross-validation. In addition, the subject-independent protocol on SEED data has achieved a state-of-the-art recognition rate, which exceeds the existing research methods. We propose a novel EEG emotion recognition framework for depression detection, which provides a robust algorithm for real-time clinical depression detection based on EEG.},
}
@article {pmid35368269,
year = {2022},
author = {Ye, H and Fan, Z and Li, G and Wu, Z and Hu, J and Sheng, X and Chen, L and Zhu, X},
title = {Spontaneous State Detection Using Time-Frequency and Time-Domain Features Extracted From Stereo-Electroencephalography Traces.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {818214},
pmid = {35368269},
issn = {1662-4548},
abstract = {As a minimally invasive recording technique, stereo-electroencephalography (SEEG) measures intracranial signals directly by inserting depth electrodes shafts into the human brain, and thus can capture neural activities in both cortical layers and subcortical structures. Despite gradually increasing SEEG-based brain-computer interface (BCI) studies, the features utilized were usually confined to the amplitude of the event-related potential (ERP) or band power, and the decoding capabilities of other time-frequency and time-domain features have not been demonstrated for SEEG recordings yet. In this study, we aimed to verify the validity of time-domain and time-frequency features of SEEG, where classification performances served as evaluating indicators. To do this, using SEEG signals under intermittent auditory stimuli, we extracted features including the average amplitude, root mean square, slope of linear regression, and line-length from the ERP trace and three traces of band power activities (high-gamma, beta, and alpha). These features were used to detect the active state (including activations to two types of names) against the idle state. Results suggested that valid time-domain and time-frequency features distributed across multiple regions, including the temporal lobe, parietal lobe, and deeper structures such as the insula. Among all feature types, the average amplitude, root mean square, and line-length extracted from high-gamma (60-140 Hz) power and the line-length extracted from ERP were the most informative. Using a hidden Markov model (HMM), we could precisely detect the onset and the end of the active state with a sensitivity of 95.7 ± 1.3% and a precision of 91.7 ± 1.6%. The valid features derived from high-gamma power and ERP in this work provided new insights into the feature selection procedure for further SEEG-based BCI applications.},
}
@article {pmid35366653,
year = {2022},
author = {Libert, A and Van Den Kerchove, A and Wittevrongel, B and Van Hulle, MM},
title = {Analytic beamformer transformation for transfer learning in motion-onset visual evoked potential decoding.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac636a},
pmid = {35366653},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials/physiology ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Support Vector Machine ; },
abstract = {Objective.While decoders of electroencephalography-based event-related potentials (ERPs) are routinely tailored to the individual user to maximize performance, developing them on populations for individual usage has proven much more challenging. We propose the analytic beamformer transformation (ABT) to extract phase and/or magnitude information from spatiotemporal ERPs in response to motion-onset stimulation.Approach.We have tested ABT on 52 motion-onset visual evoked potential (mVEP) datasets from 26 healthy subjects and compared the classification accuracy of support vector machine (SVM), spatiotemporal beamformer (stBF) and stepwise linear discriminant analysis (SWLDA) when trained on individual subjects and on a population thereof.Main results.When using phase- and combined phase/magnitude information extracted by ABT, we show significant improvements in accuracy of population-trained classifiers applied to individual users (p< 0.001). We also show that 450 epochs are needed for a correct functioning of ABT, which corresponds to 2 min of paradigm stimulation.Significance.We have shown that ABT can be used to create population-trained mVEP classifiers using a limited number of epochs. We expect this to pertain to other ERPs or synchronous stimulation paradigms, allowing for a more effective, population-based training of visual BCIs. Finally, as ABT renders recordings across subjects more structurally invariant, it could be used for transfer learning purposes in view of plug-and-play BCI applications.},
}
@article {pmid35365737,
year = {2022},
author = {Yang, J and Tang, C and Jin, R and Liu, B and Wang, P and Chen, Y and Zeng, C},
title = {Molecular mechanisms of Huanglian jiedu decoction on ulcerative colitis based on network pharmacology and molecular docking.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {5526},
pmid = {35365737},
issn = {2045-2322},
support = {No. 82060448//National Natural Science Foundation of China/ ; No. 20202ACBL206018//the Foundation of Jiangxi provincial department of Science and Technology/ ; No. 20202BAB206051//the Foundation of Jiangxi provincial department of Science and Technology/ ; No. 20161ACG70014//the Foundation of Jiangxi provincial department of Science and Technology/ ; },
mesh = {*Colitis, Ulcerative/drug therapy/genetics ; *Drugs, Chinese Herbal/chemistry ; Humans ; Molecular Docking Simulation ; Network Pharmacology ; },
abstract = {Huanglian jiedu decoction (HLJDD) is a heat-clearing and detoxifying agent composed of four kinds of Chinese herbal medicine. Previous studies have shown that HLJDD can improve the inflammatory response of ulcerative colitis (UC) and maintain intestinal barrier function. However, its molecular mechanism is not completely clear. In this study, we verified the bioactive components (BCI) and potential targets of HLJDD in the treatment of UC using network pharmacology and molecular docking, and constructed the pharmacological network and PPI network. Then the core genes were enriched by GO and KEGG. Finally, the bioactive components were docked with the key targets to verify the binding ability between them. A total of 54 active components related to UC were identified. Ten genes are very important to the PPI network. Functional analysis showed that these target genes were mainly involved in the regulation of cell response to different stimuli, IL-17 signal pathway and TNF signal pathway. The results of molecular docking showed that the active components of HLJDD had a good binding ability with the Hub gene. This study systematically elucidates the "multi-component, multi-target, multi-pathway" mechanism of anti-UC with HLJDD for the first time, suggesting that HLJDD or its active components may be candidate drugs for the treatment of ulcerative colitis.},
}
@article {pmid35364014,
year = {2022},
author = {Wandelt, SK and Kellis, S and Bjånes, DA and Pejsa, K and Lee, B and Liu, C and Andersen, RA},
title = {Decoding grasp and speech signals from the cortical grasp circuit in a tetraplegic human.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2022.03.009},
pmid = {35364014},
issn = {1097-4199},
support = {U01 NS123127/NS/NINDS NIH HHS/United States ; },
abstract = {The cortical grasp network encodes planning and execution of grasps and processes spoken and written aspects of language. High-level cortical areas within this network are attractive implant sites for brain-machine interfaces (BMIs). While a tetraplegic patient performed grasp motor imagery and vocalized speech, neural activity was recorded from the supramarginal gyrus (SMG), ventral premotor cortex (PMv), and somatosensory cortex (S1). In SMG and PMv, five imagined grasps were well represented by firing rates of neuronal populations during visual cue presentation. During motor imagery, these grasps were significantly decodable from all brain areas. During speech production, SMG encoded both spoken grasp types and the names of five colors. Whereas PMv neurons significantly modulated their activity during grasping, SMG's neural population broadly encoded features of both motor imagery and speech. Together, these results indicate that brain signals from high-level areas of the human cortex could be used for grasping and speech BMI applications.},
}
@article {pmid35362243,
year = {2022},
author = {Liang, Q and Xia, X and Sun, X and Yu, D and Huang, X and Han, G and Mugo, SM and Chen, W and Zhang, Q},
title = {Highly Stretchable Hydrogels as Wearable and Implantable Sensors for Recording Physiological and Brain Neural Signals.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e2201059},
doi = {10.1002/advs.202201059},
pmid = {35362243},
issn = {2198-3844},
support = {20210509036RQ//Jilin Province Science and Technology Development Plan/ ; 2021SYHZ0038//Jilin Province Science and Technology Development Plan/ ; 20200801008GH//Jilin Province Science and Technology Development Plan/ ; 2018YFD1100503//National Key Research and Development Program of China/ ; 2020YFA0713601//National Key Research and Development Program of China/ ; CGZHYD202012-010//Transformation Program of Scientific and Technological Achievement of the First Hospital of Jilin University and Changchun Institute of Applied Chemistry/ ; },
abstract = {Recording electrophysiological information such as brain neural signals is of great importance in health monitoring and disease diagnosis. However, foreign body response and performance loss over time are major challenges stemming from the chemomechanical mismatch between sensors and tissues. Herein, microgels are utilized as large crosslinking centers in hydrogel networks to modulate the tradeoff between modulus and fatigue resistance/stretchability for producing hydrogels that closely match chemomechanical properties of neural tissues. The hydrogels exhibit notably different characteristics compared to nanoparticles reinforced hydrogels. The hydrogels exhibit relatively low modulus, good stretchability, and outstanding fatigue resistance. It is demonstrated that the hydrogels are well suited for fashioning into wearable and implantable sensors that can obtain physiological pressure signals, record the local field potentials in rat brains, and transmit signals through the injured peripheral nerves of rats. The hydrogels exhibit good chemomechanical match to tissues, negligible foreign body response, and minimal signal attenuation over an extended time, and as such is successfully demonstrated for use as long-term implantable sensory devices. This work facilitates a deeper understanding of biohybrid interfaces, while also advancing the technical design concepts for implantable neural probes that efficiently obtain physiological information.},
}
@article {pmid35360527,
year = {2022},
author = {Yu, H and Zhao, Q and Li, S and Li, K and Liu, C and Wang, J},
title = {Decoding Digital Visual Stimulation From Neural Manifold With Fuzzy Leaning on Cortical Oscillatory Dynamics.},
journal = {Frontiers in computational neuroscience},
volume = {16},
number = {},
pages = {852281},
pmid = {35360527},
issn = {1662-5188},
abstract = {A crucial point in neuroscience is how to correctly decode cognitive information from brain dynamics for motion control and neural rehabilitation. However, due to the instability and high dimensions of electroencephalogram (EEG) recordings, it is difficult to directly obtain information from original data. Thus, in this work, we design visual experiments and propose a novel decoding method based on the neural manifold of cortical activity to find critical visual information. First, we studied four major frequency bands divided from EEG and found that the responses of the EEG alpha band (8-15 Hz) in the frontal and occipital lobes to visual stimuli occupy a prominent place. Besides, the essential features of EEG data in the alpha band are further mined via two manifold learning methods. We connect temporally consecutive brain states in the t distribution random adjacency embedded (t-SNE) map on the trial-by-trial level and find the brain state dynamics to form a cyclic manifold, with the different tasks forming distinct loops. Meanwhile, it is proved that the latent factors of brain activities estimated by t-SNE can be used for more accurate decoding and the stable neural manifold is found. Taking the latent factors of the manifold as independent inputs, a fuzzy system-based Takagi-Sugeno-Kang model is established and further trained to identify visual EEG signals. The combination of t-SNE and fuzzy learning can highly improve the accuracy of visual cognitive decoding to 81.98%. Moreover, by optimizing the features, it is found that the combination of the frontal lobe, the parietal lobe, and the occipital lobe is the most effective factor for visual decoding with 83.05% accuracy. This work provides a potential tool for decoding visual EEG signals with the help of low-dimensional manifold dynamics, especially contributing to the brain-computer interface (BCI) control, brain function research, and neural rehabilitation.},
}
@article {pmid35360289,
year = {2022},
author = {Müller-Putz, GR and Kobler, RJ and Pereira, J and Lopes-Dias, C and Hehenberger, L and Mondini, V and Martínez-Cagigal, V and Srisrisawang, N and Pulferer, H and Batistić, L and Sburlea, AI},
title = {Feel Your Reach: An EEG-Based Framework to Continuously Detect Goal-Directed Movements and Error Processing to Gate Kinesthetic Feedback Informed Artificial Arm Control.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {841312},
pmid = {35360289},
issn = {1662-5161},
abstract = {Establishing the basic knowledge, methodology, and technology for a framework for the continuous decoding of hand/arm movement intention was the aim of the ERC-funded project "Feel Your Reach". In this work, we review the studies and methods we performed and implemented in the last 6 years, which build the basis for enabling severely paralyzed people to non-invasively control a robotic arm in real-time from electroencephalogram (EEG). In detail, we investigated goal-directed movement detection, decoding of executed and attempted movement trajectories, grasping correlates, error processing, and kinesthetic feedback. Although we have tested some of our approaches already with the target populations, we still need to transfer the "Feel Your Reach" framework to people with cervical spinal cord injury and evaluate the decoders' performance while participants attempt to perform upper-limb movements. While on the one hand, we made major progress towards this ambitious goal, we also critically discuss current limitations.},
}
@article {pmid35360158,
year = {2022},
author = {Li, X and Wang, L and Miao, S and Yue, Z and Tang, Z and Su, L and Zheng, Y and Wu, X and Wang, S and Wang, J and Dou, Z},
title = {Sensorimotor Rhythm-Brain Computer Interface With Audio-Cue, Motor Observation and Multisensory Feedback for Upper-Limb Stroke Rehabilitation: A Controlled Study.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {808830},
pmid = {35360158},
issn = {1662-4548},
abstract = {Several studies have shown the positive clinical effect of brain computer interface (BCI) training for stroke rehabilitation. This study investigated the efficacy of the sensorimotor rhythm (SMR)-based BCI with audio-cue, motor observation and multisensory feedback for post-stroke rehabilitation. Furthermore, we discussed the interaction between training intensity and training duration in BCI training. Twenty-four stroke patients with severe upper limb (UL) motor deficits were randomly assigned to two groups: 2-week SMR-BCI training combined with conventional treatment (BCI Group, BG, n = 12) and 2-week conventional treatment without SMR-BCI intervention (Control Group, CG, n = 12). Motor function was measured using clinical measurement scales, including Fugl-Meyer Assessment-Upper Extremities (FMA-UE; primary outcome measure), Wolf Motor Functional Test (WMFT), and Modified Barthel Index (MBI), at baseline (Week 0), post-intervention (Week 2), and follow-up week (Week 4). EEG data from patients allocated to the BG was recorded at Week 0 and Week 2 and quantified by mu suppression means event-related desynchronization (ERD) in mu rhythm (8-12 Hz). All functional assessment scores (FMA-UE, WMFT, and MBI) significantly improved at Week 2 for both groups (p < 0.05). The BG had significantly higher FMA-UE and WMFT improvement at Week 4 compared to the CG. The mu suppression of bilateral hemisphere both had a positive trend with the motor function scores at Week 2. This study proposes a new effective SMR-BCI system and demonstrates that the SMR-BCI training with audio-cue, motor observation and multisensory feedback, together with conventional therapy may promote long-lasting UL motor improvement. Clinical Trial Registration: [http://www.chictr.org.cn], identifier [ChiCTR2000041119].},
}
@article {pmid35358959,
year = {2022},
author = {Bassi, PRAS and Attux, R},
title = {FBDNN: filter banks and deep neural networks for portable and fast brain-computer interfaces.},
journal = {Biomedical physics &amp; engineering express},
volume = {8},
number = {3},
pages = {},
doi = {10.1088/2057-1976/ac6300},
pmid = {35358959},
issn = {2057-1976},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Neural Networks, Computer ; },
abstract = {Objective.To propose novel SSVEP classification methodologies using deep neural networks (DNNs) and improve performances in single-channel and user-independent brain-computer interfaces (BCIs) with small data lengths.Approach.We propose the utilization of filter banks (creating sub-band components of the EEG signal) in conjunction with DNNs. In this context, we created three different models: a recurrent neural network (FBRNN) analyzing the time domain, a 2D convolutional neural network (FBCNN-2D) processing complex spectrum features and a 3D convolutional neural network (FBCNN-3D) analyzing complex spectrograms, which we introduce in this study as possible input for SSVEP classification. We tested our neural networks on three open datasets and conceived them so as not to require calibration from the final user, simulating a user-independent BCI.Results.The DNNs with the filter banks surpassed the accuracy of similar networks without this preprocessing step by considerable margins, and they outperformed common SSVEP classification methods (SVM and FBCCA) by even higher margins.Conclusion and significance.Filter banks allow different types of deep neural networks to more efficiently analyze the harmonic components of SSVEP. Complex spectrograms carry more information than complex spectrum features and the magnitude spectrum, allowing the FBCNN-3D to surpass the other CNNs. The performances obtained in the challenging classification problems indicates a strong potential for the construction of portable, economical, fast and low-latency BCIs.},
}
@article {pmid35358265,
year = {2022},
author = {Donoso, DA and Basset, Y and Shik, JZ and Forrister, DL and Uquillas, A and Salazar-Méndez, Y and Arizala, S and Polanco, P and Beckett, S and Dominguez G, D and Barrios, H},
title = {Male ant reproductive investment in a seasonal wet tropical forest: Consequences of future climate change.},
journal = {PloS one},
volume = {17},
number = {3},
pages = {e0266222},
pmid = {35358265},
issn = {1932-6203},
mesh = {Animals ; *Ants ; Climate Change ; Forests ; Male ; Rain ; Seasons ; Trees ; *Tropical Climate ; },
abstract = {Tropical forests sustain many ant species whose mating events often involve conspicuous flying swarms of winged gynes and males. The success of these reproductive flights depends on environmental variables and determines the maintenance of local ant diversity. However, we lack a strong understanding of the role of environmental variables in shaping the phenology of these flights. Using a combination of community-level analyses and a time-series model on male abundance, we studied male ant phenology in a seasonally wet lowland rainforest in the Panama Canal. The male flights of 161 ant species, sampled with 10 Malaise traps during 58 consecutive weeks (from August 2014 to September 2015), varied widely in number (mean = 9.8 weeks, median = 4, range = 1 to 58). Those species abundant enough for analysis (n = 97) flew mainly towards the end of the dry season and at the start of the rainy season. While litterfall, rain, temperature, and air humidity explained community composition, the time-series model estimators elucidated more complex patterns of reproductive investment across the entire year. For example, male abundance increased in weeks when maximum daily temperature increased and in wet weeks during the dry season. On the contrary, male abundance decreased in periods when rain receded (e.g., at the start of the dry season), in periods when rain fell daily (e.g., right after the beginning of the wet season), or when there was an increase in the short-term rate of litterfall (e.g., at the end of the dry season). Together, these results suggest that the BCI ant community is adapted to the dry/wet transition as the best timing of reproductive investment. We hypothesize that current climate change scenarios for tropical regions with higher average temperature, but lower rainfall, may generate phenological mismatches between reproductive flights and the adequate conditions needed for a successful start of the colony.},
}
@article {pmid35356767,
year = {2022},
author = {Liu, J and Lin, S and Li, W and Zhao, Y and Liu, D and He, Z and Wang, D and Lei, M and Hong, B and Wu, H},
title = {Ten-Hour Stable Noninvasive Brain-Computer Interface Realized by Semidry Hydrogel-Based Electrodes.},
journal = {Research (Washington, D.C.)},
volume = {2022},
number = {},
pages = {9830457},
pmid = {35356767},
issn = {2639-5274},
abstract = {Noninvasive brain-computer interface (BCI) has been extensively studied from many aspects in the past decade. In order to broaden the practical applications of BCI technique, it is essential to develop electrodes for electroencephalogram (EEG) collection with advanced characteristics such as high conductivity, long-term effectiveness, and biocompatibility. In this study, we developed a silver-nanowire/PVA hydrogel/melamine sponge (AgPHMS) semidry EEG electrode for long-lasting monitoring of EEG signal. Benefiting from the water storage capacity of PVA hydrogel, the electrolyte solution can be continuously released to the scalp-electrode interface during used. The electrolyte solution can infiltrate the stratum corneum and reduce the scalp-electrode impedance to 10 kΩ-15 kΩ. The flexible structure enables the electrode with mechanical stability, increases the wearing comfort, and reduces the scalp-electrode gap to reduce contact impedance. As a result, a long-term BCI application based on measurements of motion-onset visual evoked potentials (mVEPs) shows that the 3-hour BCI accuracy of the new electrode (77% to 100%) is approximately the same as that of conventional electrodes supported by a conductive gel during the first hour. Furthermore, the BCI system based on the new electrode can retain low contact impedance for 10 hours on scalp, which greatly improved the ability of BCI technique.},
}
@article {pmid35356325,
year = {2022},
author = {Lu, S and Yu, H},
title = {Research on Digital Business Model Innovation Based on Emotion Regulation Lens.},
journal = {Frontiers in psychology},
volume = {13},
number = {},
pages = {842076},
pmid = {35356325},
issn = {1664-1078},
abstract = {Digital technologies, such as artificial intelligence, brain-computer interfaces technology and big data, enable many firms to innovate their business model. It is clearly an emotional process due to its complex and uncertain nature, and involves individuals' emotion regulation, yet the current research lacks an effective conversion path from emotion to digital business model innovation (BMI). Drawing on theories and research on emotion regulation and business model innovation, we investigate how emotion regulation of entrepreneurs (i.e., cognitive reappraisal and expressive suppression) influence digital BMI. Data from 126 new ventures show that entrepreneurs' reappraisal positively affects digital BMI, while entrepreneurs' suppression exerts opposite effects on digital BMI. Moreover, we find that environmental dynamism moderates this relationship. The findings explain the emotional complexity in digital technology empowerment, which has implications for the development and design of brain computer interface applications and the literature on emotions and business model innovation.},
}
@article {pmid35355586,
year = {2022},
author = {Dreyer, AM and Heikkinen, BLA and Herrmann, CS},
title = {The Influence of the Modulation Index on Frequency-Modulated Steady-State Visual Evoked Potentials.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {859519},
pmid = {35355586},
issn = {1662-5161},
abstract = {Based on increased user experience during stimulation, frequency-modulated steady-state visual evoked potentials (FM-SSVEPs) have been suggested as an improved stimulation method for brain-computer interfaces. Adapting such a novel stimulation paradigm requires in-depth analyses of all different stimulation parameters and their influence on brain responses as well as the user experience during the stimulation. In the current manuscript, we assess the influence of different values for the modulation index, which determine the spectral distribution in the stimulation signal on FM-SSVEPs. We visually stimulated 14 participants at different target frequencies with four different values for the modulation index. Our results reveal that changing the modulation index in a way that elevates the stimulation power in the targeted sideband leads to increased FM-SSVEP responses. There is, however, a tradeoff with user experience as increased modulation indices also lead to increased perceived flicker intensity as well as decreased stimulation comfort in our participants. Our results can guide the choice of parameters in future FM-SSVEP implementations.},
}
@article {pmid35354131,
year = {2022},
author = {Yan, T and Suzuki, K and Kameda, S and Maeda, M and Mihara, T and Hirata, M},
title = {Electrocorticographic effects of acute ketamine on non-human primate brains.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac6293},
pmid = {35354131},
issn = {1741-2552},
mesh = {Animals ; Brain/metabolism ; Electrocorticography ; *Ketamine/pharmacology ; Primates ; Receptors, N-Methyl-D-Aspartate/metabolism ; },
abstract = {Objective. Acute blockade of glutamate N-methyl-D-aspartate receptors by ketamine induces symptoms and electrophysiological changes similar to schizophrenia. Previous studies have shown that ketamine elicits aberrant gamma oscillations in several cortical areas and impairs coupling strength between the low-frequency phase and fast frequency amplitude, which plays an important role in integrating functional information.Approach. This study utilized a customized wireless electrocorticography (ECoG) recording device to collect subdural signals from the somatosensory and primary auditory cortices in two monkeys. Ketamine was administered at a dose of 3 mg kg-1(intramuscular) or 0.56 mg kg-1(intravenous) to elicit brain oscillation reactions. We analyzed the raw data using methods such as power spectral density, time-frequency spectra, and phase-amplitude coupling (PAC).Main results. Acute ketamine triggered broadband gamma and high gamma oscillation power and decreased lower frequencies. The effect was stronger in the primary auditory cortex than in the somatosensory area. The coupling strength between the low phase of theta and the faster amplitude of gamma/high gamma bands was increased by a lower dose (0.56 mg kg-1iv) and decreased with a higher dose (3 mg kg-1im) ketamine.Significance. Our results showed that lower and higher doses of ketamine elicited differential effects on theta-gamma PAC. These findings support the utility of ECoG models as a translational platform for pharmacodynamic research in future research.},
}
@article {pmid35352525,
year = {2022},
author = {Yoldas, M},
title = {Non-invasive diagnosis of under active bladder: A pilot study.},
journal = {Archivio italiano di urologia, andrologia : organo ufficiale [di] Societa italiana di ecografia urologica e nefrologica},
volume = {94},
number = {1},
pages = {51-56},
doi = {10.4081/aiua.2022.1.51},
pmid = {35352525},
issn = {2282-4197},
mesh = {Aged, 80 and over ; Humans ; Male ; Pilot Projects ; Retrospective Studies ; *Urinary Bladder ; *Urinary Bladder Neck Obstruction/diagnosis ; Urodynamics ; },
abstract = {OBJECTIVE: We assessed the efficacy of voiding efficiency (VE) to distinguish between underactive bladder (UB) and bladder outlet obstruction (BO) without using pressure flow studies (PFS).<br><br>MATERIALS AND METHODS: in male patients, uroflowmetry and post-void residual (PVR) urine data and subsequent pressure flow studies (PFS) data were examined retrospectively. Bladder outlet obstruction index (BOI) and bladder contractility index (BCI) were calculated from patients' PFS values. Patients with BCI < 100 and BOI < 40 were grouped as UB group and patients with BCI > 100 and BOI > 40 were grouped as BOO group. VE was computed as a percentage of volume voided compared to the pre-void bladder volume.<br><br>RESULTS: In total we examined 93 patients, 44 in UB and 49 in BO group. There was no statistically significant difference between the two groups in relation to Qmax value (p = 0.38). However, total voiding time, time to reach the maximum urinary flow rate and voided volume showed statistically significant difference between the two groups (p < 0.001). Average VE was 63.6 + 2.43% and 46.2 + 2.63%) for UB and BO groups respectively and the difference was statistically significant (p < 0.001). UB can be diagnosed with at least 95% sensitivity and 88% specificity in men over age 80.<br><br>CONCLUSIONS: Non-invasive uroflowmetry and VE measurements were able to differentiate between UB and BOO patients, presenting with identical clinic features, but different findings of PFS.},
}
@article {pmid35349268,
year = {2022},
author = {Surendranath, M and Rajalekshmi, R and Ramesan, RM and Nair, P and Parameswaran, R},
title = {UV-Crosslinked Electrospun Zein/PEO Fibroporous Membranes for Wound Dressing.},
journal = {ACS applied bio materials},
volume = {5},
number = {4},
pages = {1538-1551},
doi = {10.1021/acsabm.1c01293},
pmid = {35349268},
issn = {2576-6422},
mesh = {Bandages ; Collagen ; Fibroblasts ; Humans ; Wound Healing ; *Zein/chemistry ; },
abstract = {Electrospun zein membranes are suitable for various biomedical applications. A UV-crosslinked electrospun membrane of a zein/PEO blend for wound healing application was explored in this work. The improvement in mechanical properties of the membrane after UV crosslinking was attributed to the change in protein conformation from an α-helix to a β-sheet. The circular dichroism (CD) spectra and FTIR spectra confirmed this conformational change. XRD analysis was shown to prove the amorphous nature of polymer blends with specific broad peaks at 2θ = 9° and 20°. The water vapor transmission rate (WVTR) of the membrane was found to be in the range of 1500-2000 g m-2 day-1, which was well suited with that of commercially available wound dressing material. Enough number of available functional groups like thiol, amino, and hydroxyl groups supplement a blood clotting index (BCI) to the matrix, causing 99% BCI within 4 min. A 91% cell viability result in the MTT assay with human dermal fibroblast cells confirmed the noncytotoxicity of the membrane. Tripeptides produced after the thermolysin-based hydrolysis of zein caused inhibition of TGF β1 expression and thus increased fibroblast and collagen production. The membrane stimulated 54% more collagen production compared to control cells at day 2 and caused 84% wound closure in human dermal fibroblast cells, which were desirable index markers of a potential wound care material.},
}
@article {pmid35346456,
year = {2022},
author = {Yaeger, K and Mocco, J},
title = {Future Directions of Endovascular Neurosurgery.},
journal = {Neurosurgery clinics of North America},
volume = {33},
number = {2},
pages = {233-239},
doi = {10.1016/j.nec.2021.11.007},
pmid = {35346456},
issn = {1558-1349},
mesh = {Humans ; *Neurosurgery ; Neurosurgical Procedures/methods ; },
abstract = {In the last few decades, endovascular neurosurgery has progressed from treating conventional cerebrovascular pathology to expanding outside the realm of vascular neurosurgery. As technologies, techniques, and devices are developed and refined, more patients with neurologic conditions can be treated with a less-invasive endovascular approach. For pathologies such as neurodegenerative diseases or hydrocephalus, the surgical treatment paradigm is starting to change with novel endovascular innovations. We anticipate more pathologies treatable by endovascular means, as more technological progress is made.},
}
@article {pmid35336455,
year = {2022},
author = {Rácz, M and Noboa, E and Détár, B and Nemes, Á and Galambos, P and Szűcs, L and Márton, G and Eigner, G and Haidegger, T},
title = {PlatypOUs-A Mobile Robot Platform and Demonstration Tool Supporting STEM Education.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {6},
pages = {},
pmid = {35336455},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Electromyography ; Humans ; *Robotics/methods ; Software ; Support Vector Machine ; },
abstract = {Given the rising popularity of robotics, student-driven robot development projects are playing a key role in attracting more people towards engineering and science studies. This article presents the early development process of an open-source mobile robot platform-named PlatypOUs-which can be remotely controlled via an electromyography (EMG) appliance using the MindRove brain-computer interface (BCI) headset as a sensor for the purpose of signal acquisition. The gathered bio-signals are classified by a Support Vector Machine (SVM) whose results are translated into motion commands for the mobile platform. Along with the physical mobile robot platform, a virtual environment was implemented using Gazebo (an open-source 3D robotic simulator) inside the Robot Operating System (ROS) framework, which has the same capabilities as the real-world device. This can be used for development and test purposes. The main goal of the PlatypOUs project is to create a tool for STEM education and extracurricular activities, particularly laboratory practices and demonstrations. With the physical robot, the aim is to improve awareness of STEM outside and beyond the scope of regular education programmes. It implies several disciplines, including system design, control engineering, mobile robotics and machine learning with several application aspects in each. Using the PlatypOUs platform and the simulator provides students and self-learners with a firsthand exercise, and teaches them to deal with complex engineering problems in a professional, yet intriguing way.},
}
@article {pmid35336418,
year = {2022},
author = {Wang, X and Yang, R and Huang, M},
title = {An Unsupervised Deep-Transfer-Learning-Based Motor Imagery EEG Classification Scheme for Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {6},
pages = {},
pmid = {35336418},
issn = {1424-8220},
support = {61603223//National Natural Science Foundation of China/ ; 2021//Jiangsu Provincial Qinglan Project/ ; SYG202106//Suzhou Science and Technology Programme/ ; RDF-18-02-30//Research Development Fund of XJTLU/ ; RDF-20-01-18//Research Development Fund of XJTLU/ ; KSF-E-34//Key Program Special Fund in XJTLU/ ; 20KJB520034//The Natural Science Foundation of the Jiangsu Higher Education Institutions of China/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Machine Learning ; },
abstract = {Brain-computer interface (BCI) research has attracted worldwide attention and has been rapidly developed. As one well-known non-invasive BCI technique, electroencephalography (EEG) records the brain's electrical signals from the scalp surface area. However, due to the non-stationary nature of the EEG signal, the distribution of the data collected at different times or from different subjects may be different. These problems affect the performance of the BCI system and limit the scope of its practical application. In this study, an unsupervised deep-transfer-learning-based method was proposed to deal with the current limitations of BCI systems by applying the idea of transfer learning to the classification of motor imagery EEG signals. The Euclidean space data alignment (EA) approach was adopted to align the covariance matrix of source and target domain EEG data in Euclidean space. Then, the common spatial pattern (CSP) was used to extract features from the aligned data matrix, and the deep convolutional neural network (CNN) was applied for EEG classification. The effectiveness of the proposed method has been verified through the experiment results based on public EEG datasets by comparing with the other four methods.},
}
@article {pmid35336276,
year = {2022},
author = {Sodhro, AH and Sennersten, C and Ahmad, A},
title = {Towards Cognitive Authentication for Smart Healthcare Applications.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {6},
pages = {},
pmid = {35336276},
issn = {1424-8220},
mesh = {*Biometric Identification/methods ; Biometry/methods ; Cognition ; Delivery of Health Care ; Humans ; Privacy ; },
abstract = {Secure and reliable sensing plays the key role for cognitive tracking i.e., activity identification and cognitive monitoring of every individual. Over the last years there has been an increasing interest from both academia and industry in cognitive authentication also known as biometric recognition. These are an effect of individuals' biological and physiological traits. Among various traditional biometric and physiological features, we include cognitive/brainwaves via electroencephalogram (EEG) which function as a unique performance indicator due to its reliable, flexible, and unique trait resulting in why it is hard for an un-authorized entity(ies) to breach the boundaries by stealing or mimicking them. Conventional security and privacy techniques in the medical domain are not the potential candidates to simultaneously provide both security and energy efficiency. Therefore, state-of-the art biometrics methods (i.e., machine learning, deep learning, etc.) their applications with novel solutions are investigated and recommended. The experimental setup considers EEG data analysis and interpretation of BCI. The key purpose of this setup is to reduce the number of electrodes and hence the computational power of the Random Forest (RF) classifier while testing EEG data. The performance of the random forest classifier was based on EEG datasets for 20 subjects. We found that the total number of occurred events revealed 96.1% precision in terms of chosen events.},
}
@article {pmid35332120,
year = {2022},
author = {Luo, W and Yun, D and Hu, Y and Tian, M and Yang, J and Xu, Y and Tang, Y and Zhan, Y and Xie, H and Guan, JS},
title = {Acquiring new memories in neocortex of hippocampal-lesioned mice.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {1601},
pmid = {35332120},
issn = {2041-1723},
mesh = {Animals ; Entorhinal Cortex/physiology ; Hippocampus/physiology ; Memory/physiology ; Mental Recall/physiology ; Mice ; *Neocortex/physiology ; },
abstract = {The hippocampus interacts with the neocortical network for memory retrieval and consolidation. Here, we found the lateral entorhinal cortex (LEC) modulates learning-induced cortical long-range gamma synchrony (20-40 Hz) in a hippocampal-dependent manner. The long-range gamma synchrony, which was coupled to the theta (7-10 Hz) rhythm and enhanced upon learning and recall, was mediated by inter-cortical projections from layer 5 neurons of the LEC to layer 2 neurons of the sensory and association cortices. Artificially induced cortical gamma synchrony across cortical areas improved memory encoding in hippocampal lesioned mice for originally hippocampal-dependent tasks. Mechanistically, we found that activities of cortical c-Fos labeled neurons, which showed egocentric map properties, were modulated by LEC-mediated gamma synchrony during memory recall, implicating a role of cortical synchrony to generate an integrative memory representation from disperse features. Our findings reveal the hippocampal mediated organization of cortical memories and suggest brain-machine interface approaches to improve cognitive function.},
}
@article {pmid35328418,
year = {2022},
author = {Ousingsawat, J and Centeio, R and Schreiber, R and Kunzelmann, K},
title = {Expression of SLC26A9 in Airways and Its Potential Role in Asthma.},
journal = {International journal of molecular sciences},
volume = {23},
number = {6},
pages = {},
pmid = {35328418},
issn = {1422-0067},
support = {KU756/14-1//Deutsche Forschungsgemeinschaft/ ; Mucus//Gilead Sciences (Germany)/ ; SRC013//CF-trust UK/ ; },
mesh = {Antiporters/metabolism ; *Asthma/metabolism ; Chlorides/metabolism ; *Cystic Fibrosis Transmembrane Conductance Regulator/metabolism ; Epithelial Cells/metabolism ; Humans ; Membrane Transport Proteins/metabolism ; Sulfate Transporters/genetics/metabolism ; },
abstract = {SLC26A9 is an epithelial anion transporter with a poorly defined function in airways. It is assumed to contribute to airway chloride secretion and airway surface hydration. However, immunohistochemistry showing precise localization of SLC26A9 in airways is missing. Some studies report localization near tight junctions, which is difficult to reconcile with a chloride secretory function of SLC26A9. We therefore performed immunocytochemistry of SLC26A9 in sections of human and porcine lungs. Obvious apical localization of SLC26A9 was detected in human and porcine superficial airway epithelia, whereas submucosal glands did not express SLC26A9. The anion transporter was located exclusively in ciliated epithelial cells. Highly differentiated BCi-NS1 human airway epithelial cells grown on permeable supports also expressed SLC26A9 in the apical membrane of ciliated epithelial cells. BCi-NS1 cells expressed the major Cl- transporting proteins CFTR, TMEM16A and SLC26A9 in about equal proportions and produced short-circuit currents activated by increases in intracellular cAMP or Ca2+. Both CFTR and SLC26A9 contribute to basal chloride currents in non-stimulated BCi-NS1 airway epithelia, with CFTR being the dominating Cl- conductance. In wtCFTR-expressing CFBE human airway epithelial cells, SLC26A9 was partially located in the plasma membrane, whereas CFBE cells expressing F508del-CFTR showed exclusive cytosolic localization of SLC26A9. Membrane localization of SLC26A9 and basal chloride currents were augmented by interleukin 13 in wild-type CFTR-expressing cells, but not in cells expressing the most common disease-causing mutant F508del-CFTR. The data suggest an upregulation of SLC26A9-dependent chloride secretion in asthma, but not in the presence of F508del-CFTR.},
}
@article {pmid35327887,
year = {2022},
author = {Yang, J and Gao, S and Shen, T},
title = {A Two-Branch CNN Fusing Temporal and Frequency Features for Motor Imagery EEG Decoding.},
journal = {Entropy (Basel, Switzerland)},
volume = {24},
number = {3},
pages = {},
pmid = {35327887},
issn = {1099-4300},
abstract = {With the development of technology and the rise of the meta-universe concept, the brain-computer interface (BCI) has become a hotspot in the research field, and the BCI based on motor imagery (MI) EEG has been widely concerned. However, in the process of MI-EEG decoding, the performance of the decoding model needs to be improved. At present, most MI-EEG decoding methods based on deep learning cannot make full use of the temporal and frequency features of EEG data, which leads to a low accuracy of MI-EEG decoding. To address this issue, this paper proposes a two-branch convolutional neural network (TBTF-CNN) that can simultaneously learn the temporal and frequency features of EEG data. The structure of EEG data is reconstructed to simplify the spatio-temporal convolution process of CNN, and continuous wavelet transform is used to express the time-frequency features of EEG data. TBTF-CNN fuses the features learned from the two branches and then inputs them into the classifier to decode the MI-EEG. The experimental results on the BCI competition IV 2b dataset show that the proposed model achieves an average classification accuracy of 81.3% and a kappa value of 0.63. Compared with other methods, TBTF-CNN achieves a better performance in MI-EEG decoding. The proposed method can make full use of the temporal and frequency features of EEG data and can improve the decoding accuracy of MI-EEG.},
}
@article {pmid35325878,
year = {2022},
author = {Xu, W and Gao, P and He, F and Qi, H},
title = {Improving the performance of a gaze independent P300-BCI by using the expectancy wave.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac60c8},
pmid = {35325878},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Event-Related Potentials, P300 ; Evoked Potentials ; Eye Movements ; Humans ; Photic Stimulation/methods ; },
abstract = {Objective.A P300-brain computer interface (P300-BCI) conveys a subject's intention through recognition of their event-related potentials (ERPs). However, in the case of visual stimuli, its performance depends strongly on eye gaze. When eye movement is impaired, it becomes difficult to focus attention on a target stimulus, and the quality of the ERP declines greatly, thereby affecting recognition efficiency.Approach.In this paper, the expectancy wave (E-wave) is proposed to improve signal quality and thereby improve identification of visual targets under the covert attention. The stimuli of the P300-BCI described here are presented in a fixed sequence, so the subjects can predict the next target stimulus and establish a stable expectancy effect of the target stimulus through training. Features from the E-wave that occurred 0 ∼ 300 ms before a stimulus were added to the post-stimulus ERP components for intention recognition.Main results.Comparisons of ten healthy subjects before and after training demonstrated that the expectancy wave generated before target stimulus could be used with the P300 component to improve character recognition accuracy (CRA) from 85% to 92.4%. In addition, CRA using only the expectancy component can reach 68.2%, which is significantly greater than random probability (16.7%). The results of this study indicate that the expectancy wave can be used to improve recognition efficiency for a gaze-independent P300-BCI, and that training contributes to induction and recognition of the potential.Significance.This study proposes an effective approach to an efficient gaze-independent P300-BCI system.},
}
@article {pmid35325875,
year = {2022},
author = {Fry, A and Chan, HW and Harel, NY and Spielman, LA and Escalon, MX and Putrino, DF},
title = {Evaluating the clinical benefit of brain-computer interfaces for control of a personal computer.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac60ca},
pmid = {35325875},
issn = {1741-2552},
mesh = {Activities of Daily Living ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Microcomputers ; Paralysis ; User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) enabling the control of a personal computer could provide myriad benefits to individuals with disabilities including paralysis. However, to realize this potential, these BCIs must gain regulatory approval and be made clinically available beyond research participation. Therefore, a transition from engineering-oriented to clinically oriented outcome measures will be required in the evaluation of BCIs. This review examined how to assess the clinical benefit of BCIs for the control of a personal computer. We report that: (a) a variety of different patient-reported outcome measures can be used to evaluate improvements inhow a patient feels, and we offer some considerations that should guide instrument selection. (b) Activities of daily living can be assessed to demonstrate improvements inhow a patient functions, however, new instruments that are sensitive to increases in functional independence via the ability to perform digital tasks may be needed. (c) Benefits tohow a patient surviveshas not previously been evaluated but establishing patient-initiated communication channels using BCIs might facilitate quantifiable improvements in health outcomes.},
}
@article {pmid35324445,
year = {2022},
author = {Huang, J and Yang, P and Xiong, B and Wan, B and Su, K and Zhang, ZQ},
title = {Latency Aligning Task-Related Component Analysis Using Wave Propagation for Enhancing SSVEP-Based BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {851-859},
doi = {10.1109/TNSRE.2022.3162029},
pmid = {35324445},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Neurologic Examination ; Photic Stimulation ; },
abstract = {Due to the high robustness to artifacts, steady-state visual evoked potential (SSVEP) has been widely applied to construct high-speed brain-computer interfaces (BCIs). Thus far, many spatial filtering methods have been proposed to enhance the target identification performance for SSVEP-based BCIs, and task-related component analysis (TRCA) is among the most effective ones. In this paper, we further extend TRCA and propose a new method called Latency Aligning TRCA (LA-TRCA), which aligns visual latencies on channels to obtain accurate phase information from task-related signals. Based on the SSVEP wave propagation theory, SSVEP spreads from posterior occipital areas over the cortex with a fixed phase velocity. Via estimation of the phase velocity using phase shifts of channels, the visual latencies on different channels can be determined for inter-channel alignment. TRCA is then applied to aligned data epochs for target recognition. For the validation purpose, the classification performance comparison between the proposed LA-TRCA and TRCA-based expansions were performed on two different SSVEP datasets. The experimental results illustrated that the proposed LA-TRCA method outperformed the other TRCA-based expansions, which thus demonstrated the effectiveness of the proposed approach for enhancing the SSVEP detection performance.},
}
@article {pmid35324444,
year = {2022},
author = {Pei, W and Wu, X and Zhang, X and Zha, A and Tian, S and Wang, Y and Gao, X},
title = {A Pre-Gelled EEG Electrode and Its Application in SSVEP-Based BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {843-850},
doi = {10.1109/TNSRE.2022.3161989},
pmid = {35324444},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Evoked Potentials ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {Electroencephalogram (EEG) electrodes are critical devices for brain-computer interface and neurofeedback. A pre-gelled (PreG) electrode was developed in this paper for EEG signal acquisition with a short installation time and good comfort. A hydrogel probe was placed in advance on the Ag/AgCl electrode before wearing the EEG headband instead of a time-consuming gel injection after wearing the headband. The impedance characteristics were compared between the PreG electrode and the wet electrode. The PreG electrode and the wet electrode performed the Brain-Computer Interface (BCI) application experiment to evaluate their performance. The average impedance of the PreG electrode can be decreased to 43 [Formula: see text] or even lower, which is higher than the wet electrode with an impedance of 8 [Formula: see text]. However, there is no significant difference in classification accuracy and information transmission rate (ITR) between the PreG electrode and the wet electrode in a 40 target BCI system based on Steady State Visually Evoked Potential (SSVEP). This study validated the efficiency of the proposed PreG electrode in the SSVEP-based BCI. The proposed PreG electrode will be an excellent substitute for wet electrodes in an actual application with convenience and good comfort.},
}
@article {pmid35324277,
year = {2022},
author = {Servick, K},
title = {Brain implant enables man in locked-in state to communicate.},
journal = {Science (New York, N.Y.)},
volume = {375},
number = {6587},
pages = {1327-1328},
doi = {10.1126/science.abq1706},
pmid = {35324277},
issn = {1095-9203},
mesh = {Adult ; *Amyotrophic Lateral Sclerosis/physiopathology/therapy ; *Brain ; *Brain-Computer Interfaces ; Communication ; Female ; Humans ; Male ; *Neural Prostheses ; *Neurofeedback/methods ; *Quadriplegia/therapy ; },
abstract = {Despite complete paralysis from amyotrophic lateral sclerosis, person used neural signals to spell out thoughts.},
}
@article {pmid35318316,
year = {2022},
author = {Chaudhary, U and Vlachos, I and Zimmermann, JB and Espinosa, A and Tonin, A and Jaramillo-Gonzalez, A and Khalili-Ardali, M and Topka, H and Lehmberg, J and Friehs, GM and Woodtli, A and Donoghue, JP and Birbaumer, N},
title = {Spelling interface using intracortical signals in a completely locked-in patient enabled via auditory neurofeedback training.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {1236},
pmid = {35318316},
issn = {2041-1723},
support = {DFG BI 195/77-1//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; DFG BI 195/77-1//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 16SV7701, CoMiCon//Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research)/ ; 16SV7701, CoMiCon//Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research)/ ; LUMINOUS-H2020-FETOPEN-2014-2015-RIA (686764)//EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/ ; LUMINOUS-H2020-FETOPEN-2014-2015-RIA (686764)//EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/ ; },
mesh = {*Amyotrophic Lateral Sclerosis/therapy ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Language ; Male ; *Neurofeedback ; },
abstract = {Patients with amyotrophic lateral sclerosis (ALS) can lose all muscle-based routes of communication as motor neuron degeneration progresses, and ultimately, they may be left without any means of communication. While others have evaluated communication in people with remaining muscle control, to the best of our knowledge, it is not known whether neural-based communication remains possible in a completely locked-in state. Here, we implanted two 64 microelectrode arrays in the supplementary and primary motor cortex of a patient in a completely locked-in state with ALS. The patient modulated neural firing rates based on auditory feedback and he used this strategy to select letters one at a time to form words and phrases to communicate his needs and experiences. This case study provides evidence that brain-based volitional communication is possible even in a completely locked-in state.},
}
@article {pmid35316217,
year = {2022},
author = {Najem, H and Ott, M and Kassab, C and Rao, A and Rao, G and Marisetty, A and Sonabend, AM and Horbinski, C and Verhaak, R and Shankar, A and Krishnan, SN and Varn, FS and Arrieta, VA and Gupta, P and Ferguson, SD and Huse, JT and Fuller, GN and Long, JP and Winkowski, DE and Freiberg, BA and James, CD and Platanias, LC and Lesniak, MS and Burks, JK and Heimberger, AB},
title = {Central nervous system immune interactome is function of cancer lineage, tumor microenvironment and STAT3 expression.},
journal = {JCI insight},
volume = {},
number = {},
pages = {},
doi = {10.1172/jci.insight.157612},
pmid = {35316217},
issn = {2379-3708},
abstract = {INTRODUCTION: Immune cell profiling of primary and metastatic central nervous system (CNS) tumors has been focused on the tumor, not the tumor microenvironment (TME), or have been analyzed via biopsies.<br><br>METHODS: En bloc resections of glioma (n=10) and lung metastasis (n=10) underwent tissue segmentation and high dimension opal 7-color multiplex imaging. Single cell RNA analyses inferred immune cell functionality.<br><br>RESULTS: Within gliomas, T cells were localized to the infiltrating edge and perivascular space of tumors, while residing mostly in the stroma of metastatic tumors. CD163+ macrophages were evident throughout the TME of metastatic tumors, whereas in gliomas, CD68+, CD11c+CD68+, and CD11c+CD68+CD163+ cell subtypes, were commonly observed. In lung metastases, T cells interact with CD163+ macrophages as dyads and clusters at the brain-tumor interface and within the tumor itself, and as clusters within the necrotic core. In contrast, gliomas typically lack dyad and cluster interactions, except for T cell-CD68+cell dyads within the tumor. Analysis of transcriptomic data in glioblastomas revealed that innate immune cells express both pro-inflammatory and immune suppressive gene signatures.<br><br>CONCLUSION: Our results show that immunosuppressive macrophages are abundant within the TME, and that the immune cell interactome between cancer lineages is distinct. Further, these data provide information for evaluating the role of different immune cell populations in brain tumor growth and therapeutic responses.},
}
@article {pmid35310583,
year = {2022},
author = {Song, Z and Zhan, G and Lin, Y and Fang, T and Niu, L and Zhang, X and Wang, H and Zhang, L and Jia, J and Kang, X},
title = {Electroacupuncture Alters BCI-Based Brain Network in Stroke Patients.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {8112375},
pmid = {35310583},
issn = {1687-5273},
mesh = {Brain ; *Brain-Computer Interfaces ; *Electroacupuncture ; Electroencephalography/methods ; Humans ; *Stroke/therapy ; },
abstract = {Goal. Stroke patients are usually accompanied by motor dysfunction, which greatly affects daily life. Electroacupuncture is a kind of nondrug therapy that can effectively improve motor function. However, the effect of electroacupuncture is hard to be measured immediately in clinic. This paper is aimed to reveal the instant changes in brain activity of three groups of stroke patients before, during, and after the electroacupuncture treatment by the EEG analysis in the alpha band and beta band. Methods. Seven different functional connectivity indicators including Pearson correlation coefficient, spectral coherence, mutual information, phase locking value, phase lag index, partial directed coherence, and directed transfer function were used to build the BCI-based brain network in stroke patients. Results and Conclusion. The results showed that the brain activity based on the alpha band of EEG decreased after the electroacupuncture treatment, while in the beta band of EEG, the brain activity decreased only in the first two groups. Significance. This method could be used to evaluate the effect of electroacupuncture instantly and quantitatively. The study will hopefully provide some neurophysiological evidence of the relationship between changes in brain activity and the effects of electroacupuncture. The study of BCI-based brain network changes in the alpha and beta bands before, during, and after electroacupuncture in stroke patients of different periods is helpful in adjusting and selecting the electroacupuncture regimens for different patients. The trial was registered on the Chinese clinical trial registry (ChiCTR2000036959).},
}
@article {pmid35310091,
year = {2022},
author = {Huang, R and Zeng, L and Cheng, H and Guo, X},
title = {Editorial: Neural Interface for Cognitive Human-Robot Interaction and Collaboration.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {830877},
doi = {10.3389/fnins.2022.830877},
pmid = {35310091},
issn = {1662-4548},
}
@article {pmid35306036,
year = {2022},
author = {Joshi, AA and Choi, S and Liu, Y and Chong, M and Sonkar, G and Gonzalez-Martinez, J and Nair, D and Wisnowski, JL and Haldar, JP and Shattuck, DW and Damasio, H and Leahy, RM},
title = {A hybrid high-resolution anatomical MRI atlas with sub-parcellation of cortical gyri using resting fMRI.},
journal = {Journal of neuroscience methods},
volume = {374},
number = {},
pages = {109566},
doi = {10.1016/j.jneumeth.2022.109566},
pmid = {35306036},
issn = {1872-678X},
mesh = {Brain/anatomy &amp; histology/diagnostic imaging ; Cerebral Cortex/anatomy &amp; histology/diagnostic imaging ; *Connectome/methods ; Humans ; Image Processing, Computer-Assisted/methods ; *Magnetic Resonance Imaging/methods ; Reproducibility of Results ; Rest ; },
abstract = {We present a new high-quality, single-subject atlas with sub-millimeter voxel resolution, high SNR, and excellent gray-white tissue contrast to resolve fine anatomical details. The atlas is labeled into two parcellation schemes: 1) the anatomical BCI-DNI atlas, which is manually labeled based on known morphological and anatomical features, and 2) the hybrid USCBrain atlas, which incorporates functional information to guide the sub-parcellation of cerebral cortex. In both cases, we provide consistent volumetric and cortical surface-based parcellation and labeling. The intended use of the atlas is as a reference template for structural coregistration and labeling of individual brains. A single-subject T1-weighted image was acquired five times at a resolution of 0.547 mm × 0.547 mm × 0.800 mm and averaged. Images were processed by an expert neuroanatomist using semi-automated methods in BrainSuite to extract the brain, classify tissue-types, and render anatomical surfaces. Sixty-six cortical and 29 noncortical regions were manually labeled to generate the BCI-DNI atlas. The cortical regions were further sub-parcellated into 130 cortical regions based on multi-subject connectivity analysis using resting fMRI (rfMRI) data from the Human Connectome Project (HCP) database to produce the USCBrain atlas. In addition, we provide a delineation between sulcal valleys and gyral crowns, which offer an additional set of 26 sulcal subregions per hemisphere. Lastly, a probabilistic map is provided to give users a quantitative measure of reliability for each gyral subdivision. Utility of the atlas was assessed by computing Adjusted Rand Indices (ARIs) between individual sub-parcellations obtained through structural-only coregistration to the USCBrain atlas and sub-parcellations obtained directly from each subject's resting fMRI data. Both atlas parcellations can be used with the BrainSuite, FreeSurfer, and FSL software packages.},
}
@article {pmid35304652,
year = {2022},
author = {Li, M and Zhang, N},
title = {A dynamic directed transfer function for brain functional network-based feature extraction.},
journal = {Brain informatics},
volume = {9},
number = {1},
pages = {7},
pmid = {35304652},
issn = {2198-4018},
support = {62173010//National Natural Science Foundation of China/ ; 11832003//National Natural Science Foundation of China/ ; },
abstract = {Directed transfer function (DTF) is good at characterizing the pairwise interactions from whole brain network and has been applied in discrimination of motor imagery (MI) tasks. Considering the fact that MI electroencephalogram signals are more non-stationary in frequency domain than in time domain, and the activated intensities of α band (8-13 Hz) and β band [13-30 Hz, with [Formula: see text](13-21 Hz) and [Formula: see text](21-30 Hz) included] have considerable differences for different subjects, a dynamic DTF (DDTF) with variable model order and frequency band is proposed to construct the brain functional networks (BFNs), whose information flows and outflows are further calculated as network features and evaluated by support vector machine. Extensive experiments are conducted based on a public BCI competition dataset and a real-world dataset, the highest recognition rate achieve 100% and 86%, respectively. The experimental results suggest that DDTF can reflect the dynamic evolution of BFN, the best subject-based DDTF appears in one of four frequency sub-bands (α, β, [Formula: see text] [Formula: see text]) for discrimination of MI tasks and is much more related to the current and previous states. Besides, DDTF is superior compared to granger causality-based and traditional feature extraction methods, the t-test and Kappa values show its statistical significance and high consistency as well.},
}
@article {pmid35303579,
year = {2022},
author = {Dar, MN and Akram, MU and Yuvaraj, R and Gul Khawaja, S and Murugappan, M},
title = {EEG-based emotion charting for Parkinson's disease patients using Convolutional Recurrent Neural Networks and cross dataset learning.},
journal = {Computers in biology and medicine},
volume = {144},
number = {},
pages = {105327},
doi = {10.1016/j.compbiomed.2022.105327},
pmid = {35303579},
issn = {1879-0534},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Emotions ; Humans ; Neural Networks, Computer ; *Parkinson Disease ; },
abstract = {Electroencephalogram (EEG) based emotion classification reflects the actual and intrinsic emotional state, resulting in more reliable, natural, and meaningful human-computer interaction with applications in entertainment consumption behavior, interactive brain-computer interface, and monitoring of psychological health of patients in the domain of e-healthcare. Challenges of EEG-based emotion recognition in real-world applications are variations among experimental settings and cognitive health conditions. Parkinson's Disease (PD) is the second most common neurodegenerative disorder, resulting in impaired recognition and expression of emotions. The deficit of emotional expression poses challenges for the healthcare services provided to PD patients. This study proposes 1D-CRNN-ELM architecture, which combines one-dimensional Convolutional Recurrent Neural Network (1D-CRNN) with an Extreme Learning Machine (ELM), robust for the emotion detection of PD patients, also available for cross dataset learning with various emotions and experimental settings. In the proposed framework, after EEG preprocessing, the trained CRNN can use as a feature extractor with ELM as the classifier, and again this trained CRNN can be used for learning of new emotions set with fine-tuning of other datasets. This paper also applied cross dataset learning of emotions by training with PD patients datasets and fine-tuning with publicly available datasets of AMIGOS and SEED-IV, and vice versa. Random splitting of train and test data with 80 - 20 ratio resulted in an accuracy of 97.75% for AMIGOS, 83.20% for PD, and 86.00% for HC with six basic emotion classes. Fine-tuning of trained architecture with four emotions of the SEED-IV dataset results in 92.5% accuracy. To validate the generalization of our results, leave one subject (patient) out cross-validation is also incorporated with mean accuracies of 95.84% for AMIGOS, 75.09% for PD, 77.85% for HC, and 84.97% for SEED-IV is achieved. Only a 1 - sec segment of EEG signal from 14 channels is enough to detect emotions with this performance. The proposed method outperforms state-of-the-art studies to classify EEG-based emotions with publicly available datasets, provide cross dataset learning, and validate the robustness of the deep learning framework for real-world application of psychological healthcare monitoring of Parkinson's disease patients.},
}
@article {pmid35301366,
year = {2022},
author = {Ko, W and Jeon, E and Yoon, JS and Suk, HI},
title = {Semi-supervised generative and discriminative adversarial learning for motor imagery-based brain-computer interface.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {4587},
pmid = {35301366},
issn = {2045-2322},
support = {2017-0-00451//Institute for Information and Communications Technology Promotion/ ; 2017-0-00451//Institute for Information and Communications Technology Promotion/ ; 2017-0-00451//Institute for Information and Communications Technology Promotion/ ; 2017-0-00451//Institute for Information and Communications Technology Promotion/ ; },
abstract = {Convolutional neural networks (CNNs), which can recognize structural/configuration patterns in data with different architectures, have been studied for feature extraction. However, challenges remain regarding leveraging advanced deep learning methods in BCIs. We focus on problems of small-sized training samples and interpretability of the learned parameters and leverages a semi-supervised generative and discriminative learning framework that effectively utilizes synthesized samples with real samples to discover class-discriminative features. Our framework learns the distributional characteristics of EEG signals in an embedding space using a generative model. By using artificially generated and real EEG signals, our framework finds class-discriminative spatio-temporal feature representations that help to correctly discriminate input EEG signals. It is noteworthy that the framework facilitates the exploitation of real, unlabeled samples to better uncover the underlying patterns inherent in a user's EEG signals. To validate our framework, we conducted experiments comparing our method with conventional linear models by utilizing variants of three existing CNN architectures as generator networks and measuring the performance on three public datasets. Our framework exhibited statistically significant improvements over the competing methods. We investigated the learned network via activation pattern maps and visualized generated artificial samples to empirically justify the stability and neurophysiological plausibility of our model.},
}
@article {pmid35299166,
year = {2022},
author = {Wei, W and Qiu, S and Zhang, Y and Mao, J and He, H},
title = {ERP prototypical matching net: a meta-learning method for zero-calibration RSVP-based image retrieval.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac5eb7},
pmid = {35299166},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Calibration ; Electroencephalography/methods ; Evoked Potentials/physiology ; Humans ; Learning ; },
abstract = {Objective.A rapid serial visual presentation (RSVP)-based brain-computer interface (BCI) is an efficient information detection technology through detecting event-related potentials (ERPs) evoked by target visual stimuli. The BCI system requires a time-consuming calibration process to build a reliable decoding model for a new user. Therefore, zero-calibration has become an important topic in BCI research.Approach.In this paper, we construct an RSVP dataset that includes 31 subjects, and propose a zero-calibration method based on a metric-based meta-learning: ERP prototypical matching net (EPMN). EPMN learns a metric space where the distance between electroencephalography (EEG) features and ERP prototypes belonging to the same category is smaller than that of different categories. Here, we employ prototype learning to learn a common representation from ERP templates of different subjects as ERP prototypes. Additionally, a metric-learning loss function is proposed for maximizing the distance between different classes of EEG and ERP prototypes and minimizing the distance between the same classes of EEG and ERP prototypes in the metric space.Main results.The experimental results showed that EPMN achieved a balanced-accuracy of 86.34% and outperformed the comparable methods.Significance.Our EPMN can realize zero-calibration for an RSVP-based BCI system.},
}
@article {pmid35298779,
year = {2022},
author = {Wang, T and Chen, Y and Cui, H},
title = {From Parametric Representation to Dynamical System: Shifting Views of the Motor Cortex in Motor Control.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {35298779},
issn = {1995-8218},
abstract = {In contrast to traditional representational perspectives in which the motor cortex is involved in motor control via neuronal preference for kinetics and kinematics, a dynamical system perspective emerging in the last decade views the motor cortex as a dynamical machine that generates motor commands by autonomous temporal evolution. In this review, we first look back at the history of the representational and dynamical perspectives and discuss their explanatory power and controversy from both empirical and computational points of view. Here, we aim to reconcile the above perspectives, and evaluate their theoretical impact, future direction, and potential applications in brain-machine interfaces.},
}
@article {pmid35293735,
year = {2022},
author = {Wang, T and Song, J and Liu, R and Chan, SY and Wang, K and Su, Y and Li, P and Huang, W},
title = {Motion Detecting, Temperature Alarming, and Wireless Wearable Bioelectronics Based on Intrinsically Antibacterial Conductive Hydrogels.},
journal = {ACS applied materials &amp; interfaces},
volume = {14},
number = {12},
pages = {14596-14606},
doi = {10.1021/acsami.2c00713},
pmid = {35293735},
issn = {1944-8252},
mesh = {Anti-Bacterial Agents/pharmacology ; Electric Conductivity ; Humans ; *Hydrogels/pharmacology ; Temperature ; *Wearable Electronic Devices ; },
abstract = {Hydrogels have attracted considerable interest in developing flexible bioelectronics such as wearable devices, brain-machine interface products, and health-monitoring sensors. However, these bioelectronics are always challenged by microbial contamination, which frequently reduces their service life and durability due to a lack of antibacterial property. Herein, we report a class of inherently antibacterial conductive hydrogels (ACGs) as bioelectronics for motion and temperature detection. The ACGs were composed of poly(N-isopropylacrylamide) (pNIPAM) and silver nanowires (AgNWs) via a two-step polymerization strategy, which increased the crosslink density for enhanced mechanical properties. The introduction of AgNWs improved the conductivity of ACGs and endowed them with excellent antibacterial activity against both Gram-positive and -negative bacteria. Meanwhile, pNIPAM existed in ACGs and exhibited a thermal responsive behavior, thereby inducing sharp changes in their conductivity around body temperature, which was successfully employed to assemble a temperature alarm. Moreover, ACG-based sensors exhibited excellent sensitivity (within a small strain of 5%) and the capability of capturing various motion signals (finger bending, elbow bending, and even throat vibrating). Benefiting from the superiority of ACG-based sensors, we further demonstrated a wearable wireless system for the remote control of a vehicle, which is expected to help disabled people in the future.},
}
@article {pmid35293319,
year = {2022},
author = {Li, M and Cheng, S and Fan, J and Shang, Z and Wan, H},
title = {Many heads are better than one: A multiscale neural information feature fusion framework for spatial route selections decoding from multichannel neural recordings of pigeons.},
journal = {Brain research bulletin},
volume = {184},
number = {},
pages = {1-12},
doi = {10.1016/j.brainresbull.2022.03.007},
pmid = {35293319},
issn = {1873-2747},
abstract = {The neural information at different scales exhibits spatial representations and the corresponding features are believed to be conducive for neural encoding. However, existing neural decoding studies on multiscale feature fusion have rarely been investigated. In this study, a multiscale neural information feature fusion framework is presented and we integrate these features to decode spatial routes from multichannel recordings. We design a goal-directed spatial cognitive experiment in which the pigeons need to perform a route selection task. Multichannel neural activities including spike and local field potential (LFP) recordings in the hippocampus are recorded and analyzed. The multiscale neural information features including spike firing rate features, LFP time-frequency energy features, and functional network connectivity features are extracted for spatial route decoding. Finally, we fuse the multiscale feature to solve the neural decoding problem and the results indicate that feature fusion operation improves the decoding performance significantly. Ten-fold cross-validation result analysis shows a promising improvement in the decoding performance using fusing multiscale features by an average of 0.04-0.11 at least than using any individual feature set alone. The proposed framework investigates the possibility of route decoding based on multiscale features, providing an effective way to solve the neural information decoding problems.},
}
@article {pmid35291556,
year = {2022},
author = {Willey, B and Mimmack, K and Gagliardi, G and Dossett, ML and Wang, S and Udeogu, OJ and Donovan, NJ and Gatchel, JR and Quiroz, YT and Amariglio, R and Liu, CH and Hyun, S and ElTohamy, A and Rentz, D and Sperling, RA and Marshall, GA and Vannini, P},
title = {Racial and socioeconomic status differences in stress, posttraumatic growth, and mental health in an older adult cohort during the COVID-19 pandemic.},
journal = {EClinicalMedicine},
volume = {45},
number = {},
pages = {101343},
pmid = {35291556},
issn = {2589-5370},
abstract = {Background: The COVID-19 pandemic has disproportionately impacted the most vulnerable and widened the health disparity gap in both physical and mental well-being. Consequentially, it is vital to understand how to best support elderly individuals, particularly Black Americans and people of low socioeconomic status, in navigating stressful situations during the COVID-19 pandemic and beyond. The aim of this study was to investigate perceived levels of stress, posttraumatic growth, coping strategies, socioeconomic status, and mental health between Black and non-Hispanic, White older adults, the majority over the age of 70. Additionally, we investigated which variables, if any, were associated with posttraumatic growth in these populations.<br><br>Methods: One hundred seventy-six community dwelling older adults (mean age = 76.30 ±8.94), part of two observational studies (The Harvard Aging Brain Study and Instrumental Activities of Daily Living Study) in Massachusetts, US, were included in this cross-sectional study. The survey, conducted from March 23, 2021 to May 13, 2021, measured perceived stress, behavioral coping strategies, posttraumatic growth, and mental health during the COVID-19 pandemic. We investigated associations with post-traumatic growth in a multiple linear regression model and examined their differences by race with t-tests, Wilcoxon rank-sum tests, and Fisher's exact tests. A second multiple linear regression model was used to examine which coping strategies were associated with posttraumatic growth.<br><br>Findings: Our results indicated no significant difference between the groups in terms of mental health or stress. However, Black participants showed significantly greater posttraumatic growth compared to non-Hispanic, White participants. Additionally, the coping strategies of religion and positive reframing were found to be significantly associated with posttraumatic growth. Furthermore, even with the effects of stress and coping strategies controlled for, race remained significantly associated with posttraumatic growth.<br><br>Interpretation: The COVID-19 pandemic has differentially impacted Black and non-Hispanic White older adults. These results may help encourage further analysis on geriatric psychiatry as well as understanding how cultural values and adaptations impact posttraumatic growth and mental health in diverse populations.<br><br>Funding: The Harvard Aging Brain Study (HABS) has been funded by NIH-NIA P01 AG036694 (PI: Reisa Sperling). The IADL study is funded by the National Institute on Aging (R01 AG053184, PI: Gad A. Marshall).},
}
@article {pmid35290187,
year = {2022},
author = {Kaeseler, RL and Johansson, TW and Struijk, LNSA and Jochumsen, M},
title = {Feature and Classification Analysis for Detection and Classification of Tongue Movements From Single-Trial Pre-Movement EEG.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {678-687},
doi = {10.1109/TNSRE.2022.3157959},
pmid = {35290187},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Hand ; Humans ; Movement ; Tongue ; },
abstract = {Individuals with severe tetraplegia can benefit from brain-computer interfaces (BCIs). While most movement-related BCI systems focus on right/left hand and/or foot movements, very few studies have considered tongue movements to construct a multiclass BCI. The aim of this study was to decode four movement directions of the tongue (left, right, up, and down) from single-trial pre-movement EEG and provide a feature and classifier investigation. In offline analyses (from ten individuals without a disability) detection and classification were performed using temporal, spectral, entropy, and template features classified using either a linear discriminative analysis, support vector machine, random forest or multilayer perceptron classifiers. Besides the 4-class classification scenario, all possible 3-, and 2-class scenarios were tested to find the most discriminable movement type. The linear discriminant analysis achieved on average, higher classification accuracies for both movement detection and classification. The right- and down tongue movements provided the highest and lowest detection accuracy (95.3±4.3% and 91.7±4.8%), respectively. The 4-class classification achieved an accuracy of 62.6±7.2%, while the best 3-class classification (using left, right, and up movements) and 2-class classification (using left and right movements) achieved an accuracy of 75.6±8.4% and 87.7±8.0%, respectively. Using only a combination of the temporal and template feature groups provided further classification accuracy improvements. Presumably, this is because these feature groups utilize the movement-related cortical potentials, which are noticeably different on the left- versus right brain hemisphere for the different movements. This study shows that the cortical representation of the tongue is useful for extracting control signals for multi-class movement detection BCIs.},
}
@article {pmid35287119,
year = {2022},
author = {Śliwowski, M and Martin, M and Souloumiac, A and Blanchart, P and Aksenova, T},
title = {Decoding ECoG signal into 3D hand translation using deep learning.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac5d69},
pmid = {35287119},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Deep Learning ; Electrocorticography/methods ; Electroencephalography/methods ; Hand ; Humans ; },
abstract = {Objective.Motor brain-computer interfaces (BCIs) are a promising technology that may enable motor-impaired people to interact with their environment. BCIs would potentially compensate for arm and hand function loss, which is the top priority for individuals with tetraplegia. Designing real-time and accurate BCI is crucial to make such devices useful, safe, and easy to use by patients in a real-life environment. Electrocorticography (ECoG)-based BCIs emerge as a good compromise between invasiveness of the recording device and good spatial and temporal resolution of the recorded signal. However, most ECoG signal decoders used to predict continuous hand movements are linear models. These models have a limited representational capacity and may fail to capture the relationship between ECoG signal features and continuous hand movements. Deep learning (DL) models, which are state-of-the-art in many problems, could be a solution to better capture this relationship.Approach.In this study, we tested several DL-based architectures to predict imagined 3D continuous hand translation using time-frequency features extracted from ECoG signals. The dataset used in the analysis is a part of a long-term clinical trial (ClinicalTrials.gov identifier: NCT02550522) and was acquired during a closed-loop experiment with a tetraplegic subject. The proposed architectures include multilayer perceptron, convolutional neural networks (CNNs), and long short-term memory networks (LSTM). The accuracy of the DL-based and multilinear models was compared offline using cosine similarity.Main results.Our results show that CNN-based architectures outperform the current state-of-the-art multilinear model. The best architecture exploited the spatial correlation between neighboring electrodes with CNN and benefited from the sequential character of the desired hand trajectory by using LSTMs. Overall, DL increased the average cosine similarity, compared to the multilinear model, by up to 60%, from 0.189 to 0.302 and from 0.157 to 0.249 for the left and right hand, respectively.Significance.This study shows that DL-based models could increase the accuracy of BCI systems in the case of 3D hand translation prediction in a tetraplegic subject.},
}
@article {pmid35281718,
year = {2022},
author = {Ha, J and Park, S and Im, CH},
title = {Novel Hybrid Brain-Computer Interface for Virtual Reality Applications Using Steady-State Visual-Evoked Potential-Based Brain-Computer Interface and Electrooculogram-Based Eye Tracking for Increased Information Transfer Rate.},
journal = {Frontiers in neuroinformatics},
volume = {16},
number = {},
pages = {758537},
pmid = {35281718},
issn = {1662-5196},
abstract = {Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have recently attracted increasing attention in virtual reality (VR) applications as a promising tool for controlling virtual objects or generating commands in a "hands-free" manner. Video-oculography (VOG) has been frequently used as a tool to improve BCI performance by identifying the gaze location on the screen, however, current VOG devices are generally too expensive to be embedded in practical low-cost VR head-mounted display (HMD) systems. In this study, we proposed a novel calibration-free hybrid BCI system combining steady-state visual-evoked potential (SSVEP)-based BCI and electrooculogram (EOG)-based eye tracking to increase the information transfer rate (ITR) of a nine-target SSVEP-based BCI in VR environment. Experiments were repeated on three different frequency configurations of pattern-reversal checkerboard stimuli arranged in a 3 × 3 matrix. When a user was staring at one of the nine visual stimuli, the column containing the target stimulus was first identified based on the user's horizontal eye movement direction (left, middle, or right) classified using horizontal EOG recorded from a pair of electrodes that can be readily incorporated with any existing VR-HMD systems. Note that the EOG can be recorded using the same amplifier for recording SSVEP, unlike the VOG system. Then, the target visual stimulus was identified among the three visual stimuli vertically arranged in the selected column using the extension of multivariate synchronization index (EMSI) algorithm, one of the widely used SSVEP detection algorithms. In our experiments with 20 participants wearing a commercial VR-HMD system, it was shown that both the accuracy and ITR of the proposed hybrid BCI were significantly increased compared to those of the traditional SSVEP-based BCI in VR environment.},
}
@article {pmid35273310,
year = {2022},
author = {Ali, O and Saif-Ur-Rehman, M and Dyck, S and Glasmachers, T and Iossifidis, I and Klaes, C},
title = {Enhancing the decoding accuracy of EEG signals by the introduction of anchored-STFT and adversarial data augmentation method.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {4245},
pmid = {35273310},
issn = {2045-2322},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Fourier Analysis ; Humans ; *Imagination ; Neural Networks, Computer ; },
abstract = {Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG signals are deeply hidden. State-of-the-art deep-learning algorithms are successful in learning hidden, meaningful patterns. However, the quality and the quantity of the presented inputs are pivotal. Here, we propose a feature extraction method called anchored Short Time Fourier Transform (anchored-STFT), which is an advanced version of STFT, as it minimizes the trade-off between temporal and spectral resolution presented by STFT. In addition, we propose a data augmentation method derived from l2-norm fast gradient sign method (FGSM), called gradient norm adversarial augmentation (GNAA). GNAA is not only an augmentation method but is also used to harness adversarial inputs in EEG data, which not only improves the classification accuracy but also enhances the robustness of the classifier. In addition, we also propose a CNN architecture, namely Skip-Net, for the classification of EEG signals. The proposed pipeline outperforms the current state-of-the-art methods and yields classification accuracies of 90.7% on BCI competition II dataset III and 89.5%, 81.8%, 76.0% and 85.4%, 69.1%, 80.9% on different data distributions of BCI Competition IV dataset 2b and 2a, respectively.},
}
@article {pmid35271875,
year = {2022},
author = {Zaidi, SMT and Kocatürk, S and Baykaş, T and Kocatürk, M},
title = {A behavioral paradigm for cortical control of a robotic actuator by freely moving rats in a one-dimensional two-target reaching task.},
journal = {Journal of neuroscience methods},
volume = {373},
number = {},
pages = {109555},
doi = {10.1016/j.jneumeth.2022.109555},
pmid = {35271875},
issn = {1872-678X},
mesh = {Animals ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; Movement/physiology ; Neurons/physiology ; Rats ; *Robotic Surgical Procedures ; *Robotics ; },
abstract = {BACKGROUND: Controlling the trajectory of a neuroprosthesis to reach distant targets is a commonly used brain-machine interface (BMI) task in primates and has not been available for rodents yet.<br><br>NEW METHOD: Here, we describe a novel, fine-tuned behavioral paradigm and setup which enables this task for rats in one-dimensional space for reaching two distant targets depending on their limited cognitive and visual capabilities compared to those of primates. An online transform was used to convert the activity of a pair of primary motor cortex (M1) units into two robotic actions. The rats were shaped to adapt to the transform and direct the robotic actuator toward the selected target by modulating the activity of the M1 neurons.<br><br>RESULTS: All three rats involved in the study were capable of achieving randomly selected targets with at least 78% accuracy. A total of 9 out of 16 pairs of units examined were eligible for exceeding this success criterion. Two out of three rats were capable of reversal learning, where the mapping between the activity of the M1 units and the robotic actions were reversed.<br><br>The present work is the first demonstration of trajectory-based control of a neuroprosthetic device by rodents to reach two distant targets using visual feedback.<br><br>CONCLUSION: The behavioral paradigm and setup introduced here can be used as a cost-effective platform for elucidating the information processing principles in the neural circuits related to neuroprosthetic control and for studying the performance of novel BMI technologies using freely moving rats.},
}
@article {pmid35271175,
year = {2022},
author = {Asanza, V and Peláez, E and Loayza, F and Lorente-Leyva, LL and Peluffo-Ordóñez, DH},
title = {Identification of Lower-Limb Motor Tasks via Brain-Computer Interfaces: A Topical Overview.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {5},
pages = {},
pmid = {35271175},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Lower Extremity ; Pattern Recognition, Automated ; Quality of Life ; },
abstract = {Recent engineering and neuroscience applications have led to the development of brain-computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.},
}
@article {pmid35271077,
year = {2022},
author = {Hamid, H and Naseer, N and Nazeer, H and Khan, MJ and Khan, RA and Shahbaz Khan, U},
title = {Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {5},
pages = {},
pmid = {35271077},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Discriminant Analysis ; Gait ; Humans ; Neural Networks, Computer ; Spectroscopy, Near-Infrared/methods ; },
abstract = {This research presents a brain-computer interface (BCI) framework for brain signal classification using deep learning (DL) and machine learning (ML) approaches on functional near-infrared spectroscopy (fNIRS) signals. fNIRS signals of motor execution for walking and rest tasks are acquired from the primary motor cortex in the brain's left hemisphere for nine subjects. DL algorithms, including convolutional neural networks (CNNs), long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM) are used to achieve average classification accuracies of 88.50%, 84.24%, and 85.13%, respectively. For comparison purposes, three conventional ML algorithms, support vector machine (SVM), k-nearest neighbor (k-NN), and linear discriminant analysis (LDA) are also used for classification, resulting in average classification accuracies of 73.91%, 74.24%, and 65.85%, respectively. This study successfully demonstrates that the enhanced performance of fNIRS-BCI can be achieved in terms of classification accuracy using DL approaches compared to conventional ML approaches. Furthermore, the control commands generated by these classifiers can be used to initiate and stop the gait cycle of the lower limb exoskeleton for gait rehabilitation.},
}
@article {pmid35271005,
year = {2022},
author = {Ambati, R and Raja, S and Al-Hameed, M and John, T and Arjoune, Y and Shekhar, R},
title = {Neuromorphic Architecture Accelerated Automated Seizure Detection in Multi-Channel Scalp EEG.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {5},
pages = {},
pmid = {35271005},
issn = {1424-8220},
support = {017-016//King Fahad Medical City/ ; },
mesh = {Algorithms ; Electroencephalography/methods ; *Epilepsy/diagnosis ; Humans ; *Scalp ; Seizures/diagnosis ; },
abstract = {Epileptic focal seizures can be localized in the brain using tracer injections during or immediately after the incidence of a seizure. A real-time automated seizure detection system with minimal latency can help time the injection properly to find the seizure origin accurately. Reliable real-time seizure detection systems have not been clinically reported yet. We developed an anomaly detection-based automated seizure detection system, using scalp-electroencephalogram (EEG) data, which can be trained using a few seizure sessions, and implemented it on commercially available hardware with parallel, neuromorphic architecture-the NeuroStack. We extracted nonlinear, statistical, and discrete wavelet decomposition features, and we developed a graphical user interface and traditional feature selection methods to select the most discriminative features. We investigated Reduced Coulomb Energy (RCE) networks and K-Nearest Neighbors (k-NN) for its several advantages, such as fast learning no local minima problem. We obtained a maximum sensitivity of 91.14%±1.77% and a specificity of 98.77%±0.57% with 5 s epoch duration. The system's latency was 12 s, which is within most seizure event windows, which last for an average duration of 60 s. Our results showed that the CD feature consumes large computation resources and excluding it can reduce the latency to 3.6 s but at the cost of lower performance 80% sensitivity and 97% specificity. We demonstrated that the proposed methodology achieves a high specificity and an acceptable sensitivity within a short delay. Our results indicated also that individual-based RCE are superior to population-based RCE. The proposed RCE networks has been compared to SVM and ANN as a baseline for comparison as they are the most common machine learning seizure detection methods. SVM and ANN-based systems were trained on the same data as RCE and K-NN with features optimized specifically for them. RCE nets are superior to SVM and ANN. The proposed model also achieves comparable performance to the state-of-the-art deep learning techniques while not requiring a sizeable database, which is often expensive to build. These numbers indicate that the system is viable as a trigger mechanism for tracer injection.},
}
@article {pmid35270895,
year = {2022},
author = {An, Y and Lam, HK and Ling, SH},
title = {Auto-Denoising for EEG Signals Using Generative Adversarial Network.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {5},
pages = {},
pmid = {35270895},
issn = {1424-8220},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; },
abstract = {The brain-computer interface (BCI) has many applications in various fields. In EEG-based research, an essential step is signal denoising. In this paper, a generative adversarial network (GAN)-based denoising method is proposed to denoise the multichannel EEG signal automatically. A new loss function is defined to ensure that the filtered signal can retain as much effective original information and energy as possible. This model can imitate and integrate artificial denoising methods, which reduces processing time; hence it can be used for a large amount of data processing. Compared to other neural network denoising models, the proposed model has one more discriminator, which always judges whether the noise is filtered out. The generator is constantly changing the denoising way. To ensure the GAN model generates EEG signals stably, a new normalization method called sample entropy threshold and energy threshold-based (SETET) normalization is proposed to check the abnormal signals and limit the range of EEG signals. After the denoising system is established, although the denoising model uses the different subjects' data for training, it can still apply to the new subjects' data denoising. The experiments discussed in this paper employ the HaLT public dataset. Correlation and root mean square error (RMSE) are used as evaluation criteria. Results reveal that the proposed automatic GAN denoising network achieves the same performance as the manual hybrid artificial denoising method. Moreover, the GAN network makes the denoising process automatic, representing a significant reduction in time.},
}
@article {pmid35266088,
year = {2022},
author = {Kanakaraj, P and Ramadass, K and Bao, S and Basford, M and Jones, LM and Lee, HH and Xu, K and Schilling, KG and Carr, JJ and Terry, JG and Huo, Y and Sandler, KL and Netwon, AT and Landman, BA},
title = {Workflow Integration of Research AI Tools into a Hospital Radiology Rapid Prototyping Environment.},
journal = {Journal of digital imaging},
volume = {},
number = {},
pages = {},
pmid = {35266088},
issn = {1618-727X},
support = {RadX Innovation Challenge//Vanderbilt University Medical Center/ ; ESS Innovation Challenge//Vanderbilt University Medical Center/ ; UL1 TR002243/TR/NCATS NIH HHS/United States ; },
abstract = {The field of artificial intelligence (AI) in medical imaging is undergoing explosive growth, and Radiology is a prime target for innovation. The American College of Radiology Data Science Institute has identified more than 240 specific use cases where AI could be used to improve clinical practice. In this context, thousands of potential methods are developed by research labs and industry innovators. Deploying AI tools within a clinical enterprise, even on limited retrospective evaluation, is complicated by security and privacy concerns. Thus, innovation must be weighed against the substantive resources required for local clinical evaluation. To reduce barriers to AI validation while maintaining rigorous security and privacy standards, we developed the AI Imaging Incubator. The AI Imaging Incubator serves as a DICOM storage destination within a clinical enterprise where images can be directed for novel research evaluation under Institutional Review Board approval. AI Imaging Incubator is controlled by a secure HIPAA-compliant front end and provides access to a menu of AI procedures captured within network-isolated containers. Results are served via a secure website that supports research and clinical data formats. Deployment of new AI approaches within this system is streamlined through a standardized application programming interface. This manuscript presents case studies of the AI Imaging Incubator applied to randomizing lung biopsies on chest CT, liver fat assessment on abdomen CT, and brain volumetry on head MRI.},
}
@article {pmid35265732,
year = {2022},
author = {Wyser, DG and Kanzler, CM and Salzmann, L and Lambercy, O and Wolf, M and Scholkmann, F and Gassert, R},
title = {Characterizing reproducibility of cerebral hemodynamic responses when applying short-channel regression in functional near-infrared spectroscopy.},
journal = {Neurophotonics},
volume = {9},
number = {1},
pages = {015004},
pmid = {35265732},
issn = {2329-423X},
abstract = {Significance: Functional near-infrared spectroscopy (fNIRS) enables the measurement of brain activity noninvasively. Optical neuroimaging with fNIRS has been shown to be reproducible on the group level and hence is an excellent research tool, but the reproducibility on the single-subject level is still insufficient, challenging the use for clinical applications. Aim: We investigated the effect of short-channel regression (SCR) as an approach to obtain fNIRS measurements with higher reproducibility on a single-subject level. SCR simultaneously considers contributions from long- and short-separation channels and removes confounding physiological changes through the regression of the short-separation channel information. Approach: We performed a test-retest study with a hand grasping task in 15 healthy subjects using a wearable fNIRS device, optoHIVE. Relevant brain regions were localized with transcranial magnetic stimulation to ensure correct placement of the optodes. Reproducibility was assessed by intraclass correlation, correlation analysis, mixed effects modeling, and classification accuracy of the hand grasping task. Further, we characterized the influence of SCR on reproducibility. Results: We found a high reproducibility of fNIRS measurements on a single-subject level (ICC single = 0.81 and correlation r = 0.81). SCR increased the reproducibility from 0.64 to 0.81 (ICC single) but did not affect classification (85% overall accuracy). Significant intersubject variability in the reproducibility was observed and was explained by Mayer wave oscillations and low raw signal strength. The raw signal-to-noise ratio (threshold at 40 dB) allowed for distinguishing between persons with weak and strong activations. Conclusions: We report, for the first time, that fNIRS measurements are reproducible on a single-subject level using our optoHIVE fNIRS system and that SCR improves reproducibility. In addition, we give a benchmark to easily assess the ability of a subject to elicit sufficiently strong hemodynamic responses. With these insights, we pave the way for the reliable use of fNIRS neuroimaging in single subjects for neuroscientific research and clinical applications.},
}
@article {pmid35265464,
year = {2022},
author = {Mohammadi, E and Daneshmand, PG and Khorzooghi, SMSM},
title = {Electroencephalography-Based Brain-Computer Interface Motor Imagery Classification.},
journal = {Journal of medical signals and sensors},
volume = {12},
number = {1},
pages = {40-47},
pmid = {35265464},
issn = {2228-7477},
abstract = {Background: Advances in the medical applications of brain-computer interface, like the motor imagery systems, are highly contributed to making the disabled live better. One of the challenges with such systems is to achieve high classification accuracy.<br><br>Methods: A highly accurate classification algorithm with low computational complexity is proposed here to classify different motor imageries and execution tasks. An experimental study is performed on two electroencephalography datasets (Iranian Brain-Computer Interface competition [iBCIC] dataset and the world BCI Competition IV dataset 2a) to validate the effectiveness of the proposed method. For lower complexity, the common spatial pattern is applied to decrease the 64 channel signal to four components, in addition to increase the class separability. From these components, first, some features are extracted in the time and time-frequency domains, and next, the best linear combination of these is selected by adopting the stepwise linear discriminant analysis (LDA) method, which are then applied in training and testing the classifiers including LDA, random forest, support vector machine, and K nearest neighbors. The classification strategy is of majority voting among the results of the binary classifiers.<br><br>Results: The experimental results indicate that the proposed algorithm accuracy is much higher than that of the winner of the first iBCIC. As to dataset 2a of the world BCI competition IV, the obtained results for subjects 6 and 9 outperform their counterparts. Moreover, this algorithm yields a mean kappa value of 0.53, which is higher than that of the second winner of the competition.<br><br>Conclusion: The results indicate that this method is able to classify motor imagery and execution tasks in both effective and automatic manners.},
}
@article {pmid35265111,
year = {2022},
author = {Ma, S and Dong, C and Jia, T and Ma, P and Xiao, Z and Chen, X and Zhang, L},
title = {A Feature Extraction Algorithm of Brain Network of Motor Imagination Based on a Directed Transfer Function.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {4496992},
pmid = {35265111},
issn = {1687-5273},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination ; },
abstract = {Aiming at the feature extraction of left- and right-hand movement imagination EEG signals, this paper proposes a multichannel correlation analysis method and employs the Directed Transfer Function (DTF) to identify the connectivity between different channels of EEG signals, construct a brain network, and extract the characteristics of the network information flow. Since the network information flow identified by DTF can also reflect indirect connectivity of the EEG signal networks, the newly extracted DTF features are incorporated into the traditional AR model parameter features and extend the scope of feature sets. Classifications are carried out through the Support Vector Machine (SVM). The classification results show the enlarged feature set can significantly improve the classification accuracy of the left- and right-hand motor imagery EEG signals compared to the traditional AR feature set. Finally, the EEG signals of 2 channels, 10 channels, and 32 channels were selected for comparing their different effects of classifications. The classification results showed that the multichannel analysis method was more effective. Compared with the parameter features of the traditional AR model, the network information flow features extracted by the DTF method also achieve a higher classification effect, which verifies the effectiveness of the multichannel correlation analysis method.},
}
@article {pmid35262127,
year = {2022},
author = {Xiang, W and Xie, Y and Han, Y and Long, Z and Zhang, W and Zhong, T and Liang, S and Xing, L and Xue, X and Zhan, Y},
title = {A self-powered wearable brain-machine-interface system for ceasing action.},
journal = {Nanoscale},
volume = {14},
number = {12},
pages = {4671-4678},
doi = {10.1039/d1nr08168c},
pmid = {35262127},
issn = {2040-3372},
mesh = {Animals ; Brain ; *Brain-Computer Interfaces ; Electric Power Supplies ; Electrodes ; Mice ; *Wearable Electronic Devices ; },
abstract = {A self-powered wearable brain-machine-interface system with pulse detection and brain stimulation for ceasing action has been realized. The system is composed of (1) a power supply unit that employs a piezoelectric generator and converts the mechanical energy of human daily activities into electricity; (2) a neck pulse biosensor that allows continuous measurements of carotid pulse by using a piezoelectric polyvinylidene fluoride film; (3) a data analysis module that enables a coordinated brain-machine-interface system to output brain stimulation signals; and (4) brain stimulating electrodes linked to the brain that implement behavioral intervention. Demonstration of the system with stimulating electrodes implanted in the periaqueductal gray (PAG) in running mice reveals the great effect of forced ceasing action. The mice stop their running within several seconds when the stimulation signals are sent into the PAG brain region (inducing fear). This self-powered scheme for neural stimulation realizes specific behavioral intervention without any external power supply, thus providing a new concept for future behavior intervention.},
}
@article {pmid35260273,
year = {2022},
author = {Apra, C and Serra, M and Robert, H and Carpentier, A},
title = {Early rehabilitation using gait exoskeletons is possible in the neurosurgical setting, even in patients with cognitive impairment.},
journal = {Neuro-Chirurgie},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuchi.2021.12.010},
pmid = {35260273},
issn = {1773-0619},
}
@article {pmid35259107,
year = {2022},
author = {Chen, X and Yu, Y and Tang, J and Zhou, L and Liu, K and Liu, Z and Chen, S and Wang, J and Zeng, LL and Liu, J and Hu, D},
title = {Clinical Validation of BCI-Controlled Wheelchairs in Subjects With Severe Spinal Cord Injury.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {579-589},
doi = {10.1109/TNSRE.2022.3156661},
pmid = {35259107},
issn = {1558-0210},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; *Disabled Persons ; Humans ; *Spinal Cord Injuries ; *Wheelchairs ; },
abstract = {Brain-controlled wheelchairs are one of the most promising applications that can help people gain mobility after their normal interaction pathways have been compromised by neuromuscular diseases. The feasibility of using brain signals to control wheelchairs has been well demonstrated by healthy people in previous studies. However, most potential users of brain-controlled wheelchairs are people suffering from severe physical disabilities or who are in a "locked-in" state. To further validate the clinical practicability of our previously proposed P300-based brain-controlled wheelchair, in this study, 10 subjects with severe spinal cord injuries participated in three experiments and completed ten predefined tasks in each experiment. The average accuracy and information transfer rate (ITR) were 94.8% and 4.2 bits/min, respectively. Moreover, we evaluated the physiological and cognitive burdens experienced by these individuals before and after the experiments. There were no significant changes in vital signs during the experiment, indicating minimal physiological and cognitive burden. The patients' average systolic blood pressure before and after the experiment was 113±13.7 mmHg and 114±11.9 mmHg, respectively (P = 0.122). The patients' average heart rates before and after the experiment were 79±8.4/min and 79±8.2/min, respectively (P = 0.147). The average task load, measured by the National Aeronautics and Space Administration task load index, ranged from 10.0 to 25.5. The results suggest that the proposed P300-based brain-controlled wheelchair is safe and reliable; additionally, it does not significantly increase the patient's physical and mental task burden, demonstrating its potential value in clinical applications. Our study promotes the development of a more practical brain-controlled wheelchair system.},
}
@article {pmid35257802,
year = {2022},
author = {Varela-Moreira, A and van Leur, H and Krijgsman, D and Ecker, V and Braun, M and Buchner, M and Fens, MHAM and Hennink, WE and Schiffelers, RM},
title = {Utilizing in vitro drug release assays to predict in vivo drug retention in micelles.},
journal = {International journal of pharmaceutics},
volume = {618},
number = {},
pages = {121638},
doi = {10.1016/j.ijpharm.2022.121638},
pmid = {35257802},
issn = {1873-3476},
mesh = {Albumins ; Animals ; *Drug Carriers/chemistry ; Drug Liberation ; Mice ; *Micelles ; Octoxynol ; Polyethylene Glycols/chemistry ; Polymers/chemistry ; },
abstract = {In the present work, we aim at developing an in vitro release assay to predict circulation times of hydrophobic drugs loaded into polymeric micelles (PM), upon intravenous (i.v.) administration. PM based on poly (ethylene glycol)-b-poly (N-2-benzoyloxypropyl methacrylamide) (mPEG-b-p(HPMA-Bz)) block copolymer were loaded with a panel of hydrophobic anti-cancer drugs and characterized for size, loading efficiency and release profile in different release media. Circulation times in mice of two selected drugs loaded in PM were evaluated and compared to the in vitro release profile. Release of drugs from PM was evaluated over 7 days in PBS containing Triton X-100 and in PBS containing albumin at physiological concentration (40 g/L). The results were utilized to identify crucial molecular features of the studied hydrophobic drugs leading to better micellar retention. For the best and the worst retained drugs in the in vitro assays (ABT-737 and BCI, respectively), the circulation of free and entrapped drugs into PM was examined after i.v. administration in mice. We found in vivo drug retention at 24 h post-injection similar to the retention found in the in vitro assays. This demonstrates that in vitro release assay in buffers supplemented with albumin, and to a lesser degree Triton X-100, can be employed to predict the in vivo circulation kinetics of drugs loaded in PM. Utilizing media containing acceptor molecules for hydrophobic compounds, provide a first screen to understand the stability of drug-loaded PM in the circulation and, therefore, can contribute to the reduction of animals used for circulation kinetics studies.},
}
@article {pmid35255123,
year = {2022},
author = {Lee, LO and Grodstein, F and Trudel-Fitzgerald, C and James, P and Okuzono, SS and Koga, HK and Schwartz, J and Spiro, A and Mroczek, DK and Kubzansky, LD},
title = {Optimism, Daily Stressors, and Emotional Well-Being Over Two Decades in a Cohort of Aging Men.},
journal = {The journals of gerontology. Series B, Psychological sciences and social sciences},
volume = {},
number = {},
pages = {},
doi = {10.1093/geronb/gbac025},
pmid = {35255123},
issn = {1758-5368},
support = {RF1 AG064006/AG/NIA NIH HHS/United States ; //U.S. Department of Veterans Affairs/ ; //Veterans Affairs Cooperative Studies Program/ ; R01-AG053273/NH/NIH HHS/United States ; K08 AG048221/AG/NIA NIH HHS/United States ; //Epidemiological Research Centers/ ; //Clinical Science Research and Development Service/ ; },
abstract = {OBJECTIVES: Growing evidence supports optimism as a health asset, yet how optimism influences well-being and health remains uncertain. We evaluated 1 potential pathway-the association of optimism with daily stress processes-and tested 2 hypotheses. The stressor exposure hypothesis posits that optimism would preserve emotional well-being by limiting exposure to daily stressors. The buffering hypothesis posits that higher optimism would be associated with lower emotional reactivity to daily stressors and more effective emotional recovery from them.<br><br>METHODS: Participants were 233 men from the Veterans Affairs Normative Aging Study who completed the Minnesota Multiphasic Personality Inventory-2 Revised Optimism-Pessimism scale in 1986/1991 and participated in up to three 8-day daily diary bursts in 2002-2010 (age at first burst: M = 76.7, SD = 6.5). Daily stressor occurrence, positive affect (PA), and negative affect (NA) were assessed nightly. We evaluated the hypotheses using multilevel structural equation models.<br><br>RESULTS: Optimism was unrelated to emotional reactivity to or recovery from daily stressors. Higher optimism was associated with higher average daily PA (B = 2.31, 95% Bayesian credible interval [BCI]: 1.24, 3.38) but not NA, independent of stressor exposure. Lower stressor exposure mediated the association of higher optimism with lower daily NA (indirect effect: B = -0.27, 95% BCI: -0.50, -0.09), supporting the stressor exposure hypothesis.<br><br>DISCUSSION: Findings from a sample of older men suggest that optimism may be associated with more favorable emotional well-being in later life through differences in stressor exposure rather than emotional stress response. Optimism may preserve emotional well-being among older adults by engaging emotion regulation strategies that occur relatively early in the emotion-generative process.},
}
@article {pmid35254424,
year = {2022},
author = {Dzianok, P and Antonova, I and Wojciechowski, J and Dreszer, J and Kublik, E},
title = {The Nencki-Symfonia electroencephalography/event-related potential dataset: Multiple cognitive tasks and resting-state data collected in a sample of healthy adults.},
journal = {GigaScience},
volume = {11},
number = {},
pages = {},
pmid = {35254424},
issn = {2047-217X},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Cognition/physiology ; *Electroencephalography/methods ; Evoked Potentials/physiology ; Humans ; Young Adult ; },
abstract = {BACKGROUND: One of the goals of neuropsychology is to understand the brain mechanisms underlying aspects of attention and cognitive control. Several tasks have been developed as a part of this body of research, however their results are not always consistent. A reliable comparison of the data and a synthesis of study conclusions has been precluded by multiple methodological differences. Here, we describe a publicly available, high-density electroencephalography (EEG) dataset obtained from 42 healthy young adults while they performed 3 cognitive tasks: (i) an extended multi-source interference task; (ii) a 3-stimuli oddball task; (iii) a control, simple reaction task; and (iv) a resting-state protocol. Demographic and psychometric information are included within the dataset.<br><br>DATASET VALIDATION: First, data validation confirmed acceptable quality of the obtained EEG signals. Typical event-related potential (ERP) waveforms were obtained, as expected for attention and cognitive control tasks (i.e., N200, P300, N450). Behavioral results showed the expected progression of reaction times and error rates, which confirmed the effectiveness of the applied paradigms.<br><br>CONCLUSIONS: This dataset is well suited for neuropsychological research regarding common and distinct mechanisms involved in different cognitive tasks. Using this dataset, researchers can compare a wide range of classical EEG/ERP features across tasks for any selected subset of electrodes. At the same time, 128-channel EEG recording allows for source localization and detailed connectivity studies. Neurophysiological measures can be correlated with additional psychometric data obtained from the same participants. This dataset can also be used to develop and verify novel analytical and classification approaches that can advance the field of deep/machine learning algorithms, recognition of single-trial ERP responses to different task conditions, and detection of EEG/ERP features for use in brain-computer interface applications.},
}
@article {pmid35253675,
year = {2022},
author = {Villamil, V and Wolbring, G},
title = {Influencing discussions and use of neuroadvancements as professionals and citizens: Perspectives of Canadian speech-language pathologists and audiologists.},
journal = {Work (Reading, Mass.)},
volume = {71},
number = {3},
pages = {565-584},
doi = {10.3233/WOR-205104},
pmid = {35253675},
issn = {1875-9270},
mesh = {*Audiologists ; Canada ; Humans ; Pathologists ; Speech ; *Speech-Language Pathology ; },
abstract = {BACKGROUND: Early involvement of stakeholders in neuroethics and neurogovernance discourses of neuroscientific and neurotechnological advancements is seen as essential to curtail negative consequences. Speech-language pathologists (SLPs) and audiologists (AUs) make use of neuroadvancements including cochlear implants, brain-computer interfaces, and deep-brain stimulation. Although they have a stake in neuroethics and neurogovernance discussions, they are rarely mentioned in having a role, whether as professionals or as citizens.<br><br>OBJECTIVE: The objective of the study was to explore the role of SLPs and AUs as professionals and citizens in neuroethics and neurogovernance discussions and examine the utility of lifelong learning mechanisms to learn about the implications of neuroadvancements to contribute in a meaningful way to these discussions.<br><br>METHODS: Semi-structured interviews conducted with 7 SLPs and 3 AUs were analyzed using thematic analysis.<br><br>RESULTS: Participants stated that their roles expected from them as professionals and as citizens indicate the importance to be knowledgeable on ethical, legal, and social implications of neuroadvancements and that lifelong learning is not used to learn about these implications.<br><br>CONCLUSION: More must be done to facilitate the participation of SLPs and AUs in neuroethics and neurogovernance discussions, which would enrich the neuroethics and neurogovernance discourses benefitting patients, professionals, and the public.},
}
@article {pmid35252458,
year = {2022},
author = {Sun, X and Li, M and Li, Q and Yin, H and Jiang, X and Li, H and Sun, Z and Yang, T},
title = {Poststroke Cognitive Impairment Research Progress on Application of Brain-Computer Interface.},
journal = {BioMed research international},
volume = {2022},
number = {},
pages = {9935192},
pmid = {35252458},
issn = {2314-6141},
mesh = {*Brain-Computer Interfaces ; *Cognitive Dysfunction/complications ; Electroencephalography/methods ; Humans ; Recovery of Function/physiology ; *Stroke ; *Stroke Rehabilitation/methods ; },
abstract = {Brain-computer interfaces (BCIs), a new type of rehabilitation technology, pick up nerve cell signals, identify and classify their activities, and convert them into computer-recognized instructions. This technique has been widely used in the rehabilitation of stroke patients in recent years and appears to promote motor function recovery after stroke. At present, the application of BCI in poststroke cognitive impairment is increasing, which is a common complication that also affects the rehabilitation process. This paper reviews the promise and potential drawbacks of using BCI to treat poststroke cognitive impairment, providing a solid theoretical basis for the application of BCI in this area.},
}
@article {pmid35251959,
year = {2022},
author = {Puttanawarut, C and Sirirutbunkajorn, N and Tawong, N and Jiarpinitnun, C and Khachonkham, S and Pattaranutaporn, P and Wongsawat, Y},
title = {Radiomic and Dosiomic Features for the Prediction of Radiation Pneumonitis Across Esophageal Cancer and Lung Cancer.},
journal = {Frontiers in oncology},
volume = {12},
number = {},
pages = {768152},
pmid = {35251959},
issn = {2234-943X},
abstract = {Purpose: The aim was to investigate the advantages of dosiomic and radiomic features over traditional dose-volume histogram (DVH) features for predicting the development of radiation pneumonitis (RP), to validate the generalizability of dosiomic and radiomic features by using features selected from an esophageal cancer dataset and to use these features with a lung cancer dataset.<br><br>Materials and Methods: A dataset containing 101 patients with esophageal cancer and 93 patients with lung cancer was included in this study. DVH and dosiomic features were extracted from 3D dose distributions. Radiomic features were extracted from pretreatment CT images. Feature selection was performed using only the esophageal cancer dataset. Four predictive models for RP (DVH, dosiomic, radiomic and dosiomic + radiomic models) were compared on the esophageal cancer dataset. We further used a lung cancer dataset for the external validation of the selected dosiomic and radiomic features from the esophageal cancer dataset. The performance of the predictive models was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve (ROCAUC) and the AUC of the precision recall curve (PRAUC) metrics.<br><br>Result: The ROCAUCs and PRAUCs of the DVH, dosiomic, radiomic and dosiomic + radiomic models on esophageal cancer dataset were 0.67 ± 0.11 and 0.75 ± 0.10, 0.71 ± 0.10 and 0.77 ± 0.09, 0.71 ± 0.11 and 0.79 ± 0.09, and 0.75 ± 0.10 and 0.81 ± 0.09, respectively. The predictive performance of the dosiomic- and radiomic-based models was significantly higher than that of the DVH-based model with respect to esophageal cancer. The ROCAUCs and PRAUCs of the DVH, dosiomic, radiomic and dosiomic + radiomic models on the lung cancer dataset were 0.64 ± 0.18 and 0.37 ± 0.20, 0.67 ± 0.17 and 0.37 ± 0.20, 0.67 ± 0.16 and 0.45 ± 0.23, and 0.68 ± 0.16 and 0.44 ± 0.22, respectively. On the lung cancer dataset, the predictive performance of the radiomic and dosiomic + radiomic models was significantly higher than that of the DVH-based model. However, the PRAUC of the dosiomic-based model showed no significant difference relative to the corresponding RP prediction performance on the lung cancer dataset.<br><br>Conclusion: The results suggested that dosiomic and CT radiomic features could improve RP prediction in thoracic radiotherapy. Dosiomic and radiomic feature knowledge might be transferrable from esophageal cancer to lung cancer.},
}
@article {pmid35251147,
year = {2022},
author = {Thilagaraj, M and Ramkumar, S and Arunkumar, N and Durgadevi, A and Karthikeyan, K and Hariharasitaraman, S and Rajasekaran, MP and Govindan, P},
title = {Classification of Electroencephalogram Signal for Developing Brain-Computer Interface Using Bioinspired Machine Learning Approach.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {4487254},
pmid = {35251147},
issn = {1687-5273},
mesh = {Adult ; Age Factors ; Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Machine Learning ; Neural Networks, Computer ; User-Computer Interface ; Young Adult ; },
abstract = {Transforming human intentions into patterns to direct the devices connected externally without any body movements is called Brain-Computer Interface (BCI). It is specially designed for rehabilitation patients to overcome their disabilities. Electroencephalogram (EEG) signal is one of the famous tools to operate such devices. In this study, we planned to conduct our research with twenty subjects from different age groups from 20 to 28 and 29 to 40 using three-electrode systems to analyze the performance for developing a mobile robot for navigation using band power features and neural network architecture trained with a bioinspired algorithm. From the experiment, we recognized that the maximum classification performance was 94.66% for the young group and the minimum classification performance was 94.18% for the adult group. We conducted a recognizing accuracy test for the two contrasting age groups to interpret the individual performances. The study proved that the recognition accuracy was maximum for the young group and minimum for the adult group. Through the graphical user interface, we conducted an online test for the young and adult groups. From the online test, the same young-aged people performed highly and actively with an average accuracy of 94.00% compared with the adult people whose performance was 92.00%. From this experiment, we concluded that, due to the age factor, the signal generated by the subjects decreased slightly.},
}
@article {pmid35250454,
year = {2022},
author = {Francis, JT and Rozenboym, A and von Kraus, L and Xu, S and Chhatbar, P and Semework, M and Hawley, E and Chapin, J},
title = {Similarities Between Somatosensory Cortical Responses Induced via Natural Touch and Microstimulation in the Ventral Posterior Lateral Thalamus in Macaques.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {812837},
pmid = {35250454},
issn = {1662-4548},
abstract = {Lost sensations, such as touch, could be restored by microstimulation (MiSt) along the sensory neural substrate. Such neuroprosthetic sensory information can be used as feedback from an invasive brain-machine interface (BMI) to control a robotic arm/hand, such that tactile and proprioceptive feedback from the sensorized robotic arm/hand is directly given to the BMI user. Microstimulation in the human somatosensory thalamus (Vc) has been shown to produce somatosensory perceptions. However, until recently, systematic methods for using thalamic stimulation to evoke naturalistic touch perceptions were lacking. We have recently presented rigorous methods for determining a mapping between ventral posterior lateral thalamus (VPL) MiSt, and neural responses in the somatosensory cortex (S1), in a rodent model (Choi et al., 2016; Choi and Francis, 2018). Our technique minimizes the difference between S1 neural responses induced by natural sensory stimuli and those generated via VPL MiSt. Our goal is to develop systems that know what neural response a given MiSt will produce and possibly allow the development of natural "sensation." To date, our optimization has been conducted in the rodent model and simulations. Here, we present data from simple non-optimized thalamic MiSt during peri-operative experiments, where we used MiSt in the VPL of macaques, which have a somatosensory system more like humans, as compared to our previous rat work (Li et al., 2014; Choi et al., 2016). We implanted arrays of microelectrodes across the hand area of the macaque S1 cortex as well as in the VPL. Multi and single-unit recordings were used to compare cortical responses to natural touch and thalamic MiSt in the anesthetized state. Post-stimulus time histograms were highly correlated between the VPL MiSt and natural touch modalities, adding support to the use of VPL MiSt toward producing a somatosensory neuroprosthesis in humans.},
}
@article {pmid35248817,
year = {2022},
author = {Xu, F and Xu, X and Sun, Y and Li, J and Dong, G and Wang, Y and Li, H and Wang, L and Zhang, Y and Pang, S and Yin, S},
title = {A framework for motor imagery with LSTM neural network.},
journal = {Computer methods and programs in biomedicine},
volume = {218},
number = {},
pages = {106692},
doi = {10.1016/j.cmpb.2022.106692},
pmid = {35248817},
issn = {1872-7565},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Imagination ; Neural Networks, Computer ; },
abstract = {BACKGROUND AND OBJECTIVE: How to learn robust representations from brain activities and to improve algorithm performance are the most significant issues for brain-computer interface systems.<br><br>METHODS: This study introduces a long short-term memory recurrent neural network to decode the multichannel electroencephalogram or electrocorticogram for implementing an effective motor imagery-based brain-computer interface system. The unique information processing mechanism of the long short-term memory network characterizes spatio-temporal dynamics in time sequences. This study evaluates the proposed method using publically available electroencephalogram/electrocorticogram datasets.<br><br>RESULTS: The decoded features coupled with a gradient boosting classifier could obtain high recognition accuracies of 99% for electroencephalogram and 100% for electrocorticogram, respectively.<br><br>CONCLUSIONS: The results demonstrated that the proposed model can estimate robust spatial-temporal features and obtain significant performance improvement for motor imagery-based brain-computer interface systems. Further, the proposed method is of low computational complexity.},
}
@article {pmid35241365,
year = {2022},
author = {Girges, C and Vijiaratnam, N and Zrinzo, L and Ekanayake, J and Foltynie, T},
title = {Volitional Control of Brain Motor Activity and Its Therapeutic Potential.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neurom.2022.01.007},
pmid = {35241365},
issn = {1525-1403},
abstract = {BACKGROUND: Neurofeedback training is a closed-loop neuromodulatory technique in which real-time feedback of brain activity and connectivity is provided to the participant for the purpose of volitional neural control. Through practice and reinforcement, such learning has been shown to facilitate measurable changes in brain function and behavior.<br><br>OBJECTIVES: In this review, we examine how neurofeedback, coupled with motor imagery training, has the potential to improve or normalize motor function in neurological diseases such as Parkinson disease and chronic stroke. We will also explore neurofeedback in the context of brain-machine interfaces (BMIs), discussing both noninvasive and invasive methods which have been used to power external devices (eg, robot hand orthosis or exoskeleton) in the context of motor neurorehabilitation.<br><br>CONCLUSIONS: The published literature provides mounting high-quality evidence that neurofeedback and BMI control may lead to clinically relevant changes in brain function and behavior.<br><br>CLINICAL TRIAL REGISTRATION: The ClinicalTrials.gov registration number for the study is NCT00912041.},
}
@article {pmid35237140,
year = {2022},
author = {Rahman, ML and Files, BT and Oiknine, AH and Pollard, KA and Khooshabeh, P and Song, C and Passaro, AD},
title = {Combining Neural and Behavioral Measures Enhances Adaptive Training.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {787576},
pmid = {35237140},
issn = {1662-5161},
abstract = {Adaptive training adjusts a training task with the goal of improving learning outcomes. Adaptive training has been shown to improve human performance in attention, working memory capacity, and motor control tasks. Additionally, correlations have been observed between neural EEG spectral features (4-13 Hz) and the performance of some cognitive tasks. This relationship suggests some EEG features may be useful in adaptive training regimens. Here, we anticipated that adding a neural measure into a behavioral-based adaptive training system would improve human performance on a subsequent transfer task. We designed, developed, and conducted a between-subjects study of 44 participants comparing three training regimens: Single Item Fixed Difficulty (SIFD), Behaviorally Adaptive Training (BAT), and Combined Adaptive Training (CAT) using both behavioral and EEG measures. Results showed a statistically significant transfer task performance advantage of the CAT-based system relative to SIFD and BAT systems of 6 and 9 percentage points, respectively. Our research shows a promising pathway for designing closed-loop BCI systems based on both users' behavioral performance and neural signals for augmenting human performance.},
}
@article {pmid35235515,
year = {2022},
author = {Liu, C and Jin, J and Daly, I and Li, S and Sun, H and Huang, Y and Wang, X and Cichocki, A},
title = {SincNet-Based Hybrid Neural Network for Motor Imagery EEG Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {540-549},
doi = {10.1109/TNSRE.2022.3156076},
pmid = {35235515},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; *Imagination ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; },
abstract = {It is difficult to identify optimal cut-off frequencies for filters used with the common spatial pattern (CSP) method in motor imagery (MI)-based brain-computer interfaces (BCIs). Most current studies choose filter cut-frequencies based on experience or intuition, resulting in sub-optimal use of MI-related spectral information in the electroencephalography (EEG). To improve information utilization, we propose a SincNet-based hybrid neural network (SHNN) for MI-based BCIs. First, raw EEG is segmented into different time windows and mapped into the CSP feature space. Then, SincNets are used as filter bank band-pass filters to automatically filter the data. Next, we used squeeze-and-excitation modules to learn a sparse representation of the filtered data. The resulting sparse data were fed into convolutional neural networks to learn deep feature representations. Finally, these deep features were fed into a gated recurrent unit module to seek sequential relations, and a fully connected layer was used for classification. We used the BCI competition IV datasets 2a and 2b to verify the effectiveness of our SHNN method. The mean classification accuracies (kappa values) of our SHNN method are 0.7426 (0.6648) on dataset 2a and 0.8349 (0.6697) on dataset 2b, respectively. The statistical test results demonstrate that our SHNN can significantly outperform other state-of-the-art methods on these datasets.},
}
@article {pmid35234849,
year = {2022},
author = {Mahmud, S and Paul, GK and Biswas, S and Kazi, T and Mahbub, S and Mita, MA and Afrose, S and Islam, A and Ahaduzzaman, S and Hasan, R and Shimu, SS and Promi, MM and Shehab, MN and Rahman, E and Sujon, KM and Alom, W and Modak, A and Zaman, S and Uddin, S and Emran, TB and Islam, S and Saleh, A},
title = {phytochemdb: a platform for virtual screening and computer-aided drug designing.},
journal = {Database : the journal of biological databases and curation},
volume = {2022},
number = {2022},
pages = {},
doi = {10.1093/database/baac002},
pmid = {35234849},
issn = {1758-0463},
mesh = {Computers ; Databases, Factual ; Drug Design ; Phytochemicals/chemistry/pharmacology ; *Plants, Medicinal ; },
abstract = {The phytochemicals of medicinal plants are regarded as a rich source of diverse chemical spaces that have been used as supplements and alternative medicines in the millennium. Even in this era of combinatorial chemical drugs, phytomedicines account for a large share of the statistics of newly approved drugs. In the field of computational aided and rational drug design, there is an urgent need to develop and build a useful phytochemical database management system with a user-friendly interface that allows proper data storage, retrieval and management. We showed 'phytochemdb', a manually managed database that compiles 525 plants and their corresponding 8093 phytochemicals, aiming to incorporate the activities of phytochemicals from medicinal plants. The database collects molecular formula, three-dimensional/two-dimensional structure, canonical SMILES, molecular weight, no. of heavy atoms, no. of aromatic heavy atoms, fraction Csp3, no. of rotatable bonds, no. of H-bond acceptors, no. of H-bond donors, molar refractivity, topological polar surface area, gastrointestinal absorption, Blood-Brain Barrier (BBB) permeant, P-gp substrate, CYP1A2 inhibitor, CYP2C19 inhibitor, CYP2C9 inhibitor, CYP2D6 inhibitor, CYP3A4 inhibitor, Log Kp, Ghose, Veber, Egan, Muegge, bioavailability scores, pan-assay interference compounds, Brenk, Leadlikeness, synthetic accessibility, iLOGP and Lipinski rule of five with the number of violations for each compound. It provides open contribution functions for the researchers who screen phytochemicals in the laboratory and have released their data. 'phytochemdb' is a comprehensive database that gathers most of the information about medicinal plants in one platform, which is considered to be very beneficial to the work of researchers on medicinal plants. 'phytochemdb' is available for free at https://phytochemdb.com/.},
}
@article {pmid35234668,
year = {2022},
author = {Li, P and Li, C and Bore, JC and Si, Y and Li, F and Cao, Z and Zhang, Y and Wang, G and Zhang, Z and Yao, D and Xu, P},
title = {L1-norm based time-varying brain neural network and its application to dynamic analysis for motor imagery.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac59a4},
pmid = {35234668},
issn = {1741-2552},
mesh = {*Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination ; Neural Networks, Computer ; },
abstract = {Objective.Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interface offers a promising way to improve the efficiency of motor rehabilitation and motor skill learning. In recent years, the power of dynamic network analysis for MI classification has been proved. In fact, its usability mainly depends on the accurate estimation of brain connection. However, traditional dynamic network estimation strategies such as adaptive directed transfer function (ADTF) are designed in the L2-norm. Usually, they estimate a series of pseudo connections caused by outliers, which results in biased features and further limits its online application. Thus, how to accurately infer dynamic causal relationship under outlier influence is urgent.Approach.In this work, we proposed a novel ADTF, which solves the dynamic system in the L1-norm space (L1-ADTF), so as to restrict the outlier influence. To enhance its convergence, we designed an iteration strategy with the alternating direction method of multipliers, which could be used for the solution of the dynamic state-space model restricted in the L1-norm space. Furthermore, we compared L1-ADTF to traditional ADTF and its dual extension across both simulation and real EEG experiments.Main results.A quantitative comparison between L1-ADTF and other ADTFs in simulation studies demonstrates that fewer bias errors and more desirable dynamic state transformation patterns can be captured by the L1-ADTF. Application to real MI EEG datasets seriously noised by ocular artifacts also reveals the efficiency of the proposed L1-ADTF approach to extract the time-varying brain neural network patterns, even when more complex noises are involved.Significance.The L1-ADTF may not only be capable of tracking time-varying brain network state drifts robustly but may also be useful in solving a wide range of dynamic systems such as trajectory tracking problems and dynamic neural networks.},
}
@article {pmid35234665,
year = {2022},
author = {Moly, A and Costecalde, T and Martel, F and Martin, M and Larzabal, C and Karakas, S and Verney, A and Charvet, G and Chabardes, S and Benabid, AL and Aksenova, T},
title = {An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac59a0},
pmid = {35234665},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electrocorticography/methods ; Epidural Space ; *Exoskeleton Device ; Humans ; Linear Models ; },
abstract = {Objective.The article aims at addressing 2 challenges to step motor brain-computer interface (BCI) out of laboratories: asynchronous control of complex bimanual effectors with large numbers of degrees of freedom, using chronic and safe recorders, and the decoding performance stability over time without frequent decoder recalibration.Approach.Closed-loop adaptive/incremental decoder training is one strategy to create a model stable over time. Adaptive decoders update their parameters with new incoming data, optimizing the model parameters in real time. It allows cross-session training with multiple recording conditions during closed loop BCI experiments. In the article, an adaptive tensor-based recursive exponentially weighted Markov-switching multi-linear model (REW-MSLM) decoder is proposed. REW-MSLM uses a mixture of expert (ME) architecture, mixing or switching independent decoders (experts) according to the probability estimated by a 'gating' model. A Hidden Markov model approach is employed as gating model to improve the decoding robustness and to provide strong idle state support. The ME architecture fits the multi-limb paradigm associating an expert to a particular limb or action.Main results.Asynchronous control of an exoskeleton by a tetraplegic patient using a chronically implanted epidural electrocorticography (EpiCoG) recorder is reported. The stable over a period of six months (without decoder recalibration) eight-dimensional alternative bimanual control of the exoskeleton and its virtual avatar is demonstrated.Significance.Based on the long-term (>36 months) chronic bilateral EpiCoG recordings in a tetraplegic (ClinicalTrials.gov, NCT02550522), we addressed the poorly explored field of asynchronous bimanual BCI. The new decoder was designed to meet to several challenges: the high-dimensional control of a complex effector in experiments closer to real-world behavior (point-to-point pursuit versus conventional center-out tasks), with the ability of the BCI system to act as a stand-alone device switching between idle and control states, and a stable performance over a long period of time without decoder recalibration.},
}
@article {pmid35231982,
year = {2022},
author = {Xu, H and Gong, A and Ding, P and Luo, J and Chen, C and Fu, Y},
title = {[Key technologies for intelligent brain-computer interaction based on magnetoencephalography].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {39},
number = {1},
pages = {198-206},
doi = {10.7507/1001-5515.202108069},
pmid = {35231982},
issn = {1001-5515},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; *Magnetoencephalography ; Technology ; },
abstract = {Brain-computer interaction (BCI) is a transformative human-computer interaction, which aims to bypass the peripheral nerve and muscle system and directly convert the perception, imagery or thinking activities of cranial nerves into actions for further improving the quality of human life. Magnetoencephalogram (MEG) measures the magnetic field generated by the electrical activity of neurons. It has the unique advantages of non-contact measurement, high temporal and spatial resolution, and convenient preparation. It is a new BCI driving signal. MEG-BCI research has important brain science significance and potential application value. So far, few documents have elaborated the key technical issues involved in MEG-BCI. Therefore, this paper focuses on the key technologies of MEG-BCI, and details the signal acquisition technology involved in the practical MEG-BCI system, the design of the MEG-BCI experimental paradigm, the MEG signal analysis and decoding key technology, MEG-BCI neurofeedback technology and its intelligent method. Finally, this paper also discusses the existing problems and future development trends of MEG-BCI. It is hoped that this paper will provide more useful ideas for MEG-BCI innovation research.},
}
@article {pmid35231981,
year = {2022},
author = {Zhang, Y and Xia, M and Chen, K and Xu, P and Yao, D},
title = {[Progresses and prospects on frequency recognition methods for steady-state visual evoked potential].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {39},
number = {1},
pages = {192-197},
doi = {10.7507/1001-5515.202102031},
pmid = {35231981},
issn = {1001-5515},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Photic Stimulation ; },
abstract = {Steady-state visual evoked potential (SSVEP) is one of the commonly used control signals in brain-computer interface (BCI) systems. The SSVEP-based BCI has the advantages of high information transmission rate and short training time, which has become an important branch of BCI research field. In this review paper, the main progress on frequency recognition algorithm for SSVEP in past five years are summarized from three aspects, i.e., unsupervised learning algorithms, supervised learning algorithms and deep learning algorithms. Finally, some frontier topics and potential directions are explored.},
}
@article {pmid35231964,
year = {2022},
author = {Cui, Y and Xie, S and Xie, X and Duan, X and Gao, C},
title = {[A spatial-temporal hybrid feature extraction method for rapid serial visual presentation of electroencephalogram signals].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {39},
number = {1},
pages = {39-46},
doi = {10.7507/1001-5515.202104049},
pmid = {35231964},
issn = {1001-5515},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Principal Component Analysis ; Signal Processing, Computer-Assisted ; },
abstract = {Rapid serial visual presentation-brain computer interface (RSVP-BCI) is the most popular technology in the early discover task based on human brain. This algorithm can obtain the rapid perception of the environment by human brain. Decoding brain state based on single-trial of multichannel electroencephalogram (EEG) recording remains a challenge due to the low signal-to-noise ratio (SNR) and nonstationary. To solve the problem of low classification accuracy of single-trial in RSVP-BCI, this paper presents a new feature extraction algorithm which uses principal component analysis (PCA) and common spatial pattern (CSP) algorithm separately in spatial domain and time domain, creating a spatial-temporal hybrid CSP-PCA (STHCP) algorithm. By maximizing the discrimination distance between target and non-target, the feature dimensionality was reduced effectively. The area under the curve (AUC) of STHCP algorithm is higher than that of the three benchmark algorithms (SWFP, CSP and PCA) by 17.9%, 22.2% and 29.2%, respectively. STHCP algorithm provides a new method for target detection.},
}
@article {pmid35231963,
year = {2022},
author = {Xu, D and Li, M},
title = {[Parameter transfer learning based on shallow visual geometry group network and its application in motor imagery classification].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {39},
number = {1},
pages = {28-38},
doi = {10.7507/1001-5515.202108060},
pmid = {35231963},
issn = {1001-5515},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Machine Learning ; },
abstract = {Transfer learning is provided with potential research value and application prospect in motor imagery electroencephalography (MI-EEG)-based brain-computer interface (BCI) rehabilitation system, and the source domain classification model and transfer strategy are the two important aspects that directly affect the performance and transfer efficiency of the target domain model. Therefore, we propose a parameter transfer learning method based on shallow visual geometry group network (PTL-sVGG). First, Pearson correlation coefficient is used to screen the subjects of the source domain, and the short-time Fourier transform is performed on the MI-EEG data of each selected subject to acquire the time-frequency spectrogram images (TFSI). Then, the architecture of VGG-16 is simplified and the block design is carried out, and the modified sVGG model is pre-trained with TFSI of source domain. Furthermore, a block-based frozen-fine-tuning transfer strategy is designed to quickly find and freeze the block with the greatest contribution to sVGG model, and the remaining blocks are fine-tuned by using TFSI of target subjects to obtain the target domain classification model. Extensive experiments are conducted based on public MI-EEG datasets, the average recognition rate and Kappa value of PTL-sVGG are 94.9% and 0.898, respectively. The results show that the subjects' optimization is beneficial to improve the model performance in source domain, and the block-based transfer strategy can enhance the transfer efficiency, realizing the rapid and effective transfer of model parameters across subjects on the datasets with different number of channels. It is beneficial to reduce the calibration time of BCI system, which promote the application of BCI technology in rehabilitation engineering.},
}
@article {pmid35231052,
year = {2022},
author = {Fossi, LD and Debien, C and Demarty, AL and Vaiva, G and Messiah, A},
title = {Loss to follow-up in a population-wide brief contact intervention to prevent suicide attempts - The VigilanS program, France.},
journal = {PloS one},
volume = {17},
number = {3},
pages = {e0263379},
pmid = {35231052},
issn = {1932-6203},
mesh = {*Suicide, Attempted ; },
abstract = {BACKGROUND: Brief Contact Interventions (BCIs) after a suicide attempt (SA) are an important element of prevention against SA and suicide. VigilanS generalizes to a whole French region a BCI combining resource cards, telephone calls and sending postcards, according to a predefined algorithm. However, a major obstacle to such real-life intervention is the loss of contact during follow-up. Here, we analyze the occurrence of loss of follow-up (LFU) and compare characteristics of patients LFU with follow-up completers.<br><br>METHODS: The study concerned patients included in VigilanS over the period from 1st January 2015 to 31 December 2018, with an end of follow-up on 1st July 2019. We performed a series of descriptive analysis and logistic regressions. The outcome was the loss to follow-up, relative to the 6th month call marking the end of the follow-up; the predictive variables were the characteristics of the patient at entry and during follow-up. Age and sex were considered as adjustment variables.<br><br>RESULTS: 11879 inclusions occurred during the study period, corresponding to 10666 different patients. The mean age was 40.6 ± 15 years. More than a third were non-first suicide attempters (46.6%) and the most frequent means of suicide was by voluntary drug intoxication (83.2%). 8335 patients were LFU. After simple and multiple regression, a significant relationship with loss to follow-up was identified among non-first suicide attempters, alcohol consumers, patients having no companion on arrival at the emergency room, patients who didn't make or receive any calls. An increased stay in hospital after a SA was a protective factor against loss of follow-up.<br><br>CONCLUSION: A majority of patients were lost to follow-up by the expected surveillance time of 6 months. Characteristics of lost patients will help focusing efforts to improve retention in the VigilanS program and might give insights for BCI implemented elsewhere.},
}
@article {pmid35226599,
year = {2022},
author = {Corsi, MC and Chevallier, S and De Vico Fallani, F and Yger, F},
title = {Functional connectivity ensemble method to enhance BCI performance (FUCONE).},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2022.3154885},
pmid = {35226599},
issn = {1558-2531},
abstract = {OBJECTIVE: Relying on the idea that functional connectivity provides important insights on the underlying dynamic of neuronal interactions, we propose a novel framework that combines functional connectivity estimators and covariance-based pipelines to improve the classification of mental states, such as motor imagery.<br><br>METHODS: A Riemannian classifier is trained for each estimator and an ensemble classifier combines the decisions in each feature space. A thorough assessment of the functional connectivity estimators is provided and the best performing pipeline among those tested, called FUCONE, is evaluated on different conditions and datasets.<br><br>RESULTS: Using a meta-analysis to aggregate results across datasets, FUCONE performed significantly better than all state-of-the-art methods.<br><br>CONCLUSION: The performance gain is mostly imputable to the improved diversity of the feature spaces, increasing the robustness of the ensemble classifier with respect to the inter- and intra-subject variability.<br><br>SIGNIFICANCE: Our results offer new insights into the need to consider functional connectivity-based methods to improve the BCI performance.},
}
@article {pmid35223807,
year = {2021},
author = {Hou, Y and Jia, S and Lun, X and Zhang, S and Chen, T and Wang, F and Lv, J},
title = {Deep Feature Mining via the Attention-Based Bidirectional Long Short Term Memory Graph Convolutional Neural Network for Human Motor Imagery Recognition.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {9},
number = {},
pages = {706229},
pmid = {35223807},
issn = {2296-4185},
abstract = {Recognition accuracy and response time are both critically essential ahead of building the practical electroencephalography (EEG)-based brain-computer interface (BCI). However, recent approaches have compromised either the classification accuracy or the responding time. This paper presents a novel deep learning approach designed toward both remarkably accurate and responsive motor imagery (MI) recognition based on scalp EEG. Bidirectional long short-term memory (BiLSTM) with the attention mechanism is employed, and the graph convolutional neural network (GCN) promotes the decoding performance by cooperating with the topological structure of features, which are estimated from the overall data. Particularly, this method is trained and tested on the short EEG recording with only 0.4 s in length, and the result has shown effective and efficient prediction based on individual and groupwise training, with 98.81% and 94.64% accuracy, respectively, which outperformed all the state-of-the-art studies. The introduced deep feature mining approach can precisely recognize human motion intents from raw and almost-instant EEG signals, which paves the road to translate the EEG-based MI recognition to practical BCI systems.},
}
@article {pmid35223798,
year = {2022},
author = {Hu, Y and Liu, N and Chen, K and Liu, M and Wang, F and Liu, P and Zhang, Y and Zhang, T and Xiao, X},
title = {Resilient and Self-Healing Hyaluronic Acid/Chitosan Hydrogel With Ion Conductivity, Low Water Loss, and Freeze-Tolerance for Flexible and Wearable Strain Sensor.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {10},
number = {},
pages = {837750},
pmid = {35223798},
issn = {2296-4185},
abstract = {Conductive hydrogel is a vital candidate for the fabrication of flexible and wearable electric sensors due to its good designability and biocompatibility. These well-designed conductive hydrogel-based flexible strain sensors show great potential in human motion monitoring, artificial skin, brain computer interface (BCI), and so on. However, easy drying and freezing of conductive hydrogels with high water content greatly limited their further application. Herein, we proposed a natural polymer-based conductive hydrogel with excellent mechanical property, low water loss, and freeze-tolerance. The main hydrogel network was formed by the Schiff base reaction between the hydrazide-grafted hyaluronic acid and the oxidized chitosan, and the added KCl worked as the conductive filler. The reversible crosslinking in the prepared hydrogel resulted in its resilience and self-healing feature. At the same time, the synthetic effect of KCl and glycerol endowed our hydrogel with outstanding anti-freezing property, while glycerol also endowed this hydrogel with anti-drying property. When this hydrogel was assembled as a flexible strain sensor, it showed good sensitivity (GF = 2.64), durability, and stability even under cold condition (-37°C).},
}
@article {pmid35221886,
year = {2021},
author = {Liu, Y and Huang, S and Wang, Z and Ji, F and Ming, D},
title = {Functional Reorganization After Four-Week Brain-Computer Interface-Controlled Supernumerary Robotic Finger Training: A Pilot Study of Longitudinal Resting-State fMRI.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {766648},
pmid = {35221886},
issn = {1662-4548},
abstract = {Humans have long been fascinated by the opportunities afforded through motor augmentation provided by the supernumerary robotic fingers (SRFs) and limbs (SRLs). However, the neuroplasticity mechanism induced by the motor augmentation equipment still needs further investigation. This study focused on the resting-state brain functional reorganization during longitudinal brain-computer interface (BCI)-controlled SRF training in using the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and degree centrality (DC) metrics. Ten right-handed subjects were enrolled for 4 weeks of BCI-controlled SRF training. The behavioral data and the neurological changes were recorded at baseline, training for 2 weeks, training for 4 weeks immediately after, and 2 weeks after the end of training. One-way repeated-measure ANOVA was used to investigate long-term motor improvement [F(2.805,25.24) = 43.94, p < 0.0001] and neurological changes. The fALFF values were significantly modulated in Cerebelum_6_R and correlated with motor function improvement (r = 0.6887, p < 0.0402) from t0 to t2. Besides, Cerebelum_9_R and Vermis_3 were also significantly modulated and showed different trends in longitudinal SRF training in using ReHo metric. At the same time, ReHo values that changed from t0 to t1 in Vermis_3 was significantly correlated with motor function improvement (r = 0.7038, p < 0.0344). We conclude that the compensation and suppression mechanism of the cerebellum existed during BCI-controlled SRF training, and this current result provided evidence to the neuroplasticity mechanism brought by the BCI-controlled motor-augmentation devices.},
}
@article {pmid35219429,
year = {2022},
author = {Vendrell-Llopis, N and Fang, C and Qü, AJ and Costa, RM and Carmena, JM},
title = {Diverse operant control of different motor cortex populations during learning.},
journal = {Current biology : CB},
volume = {32},
number = {7},
pages = {1616-1622.e5},
pmid = {35219429},
issn = {1879-0445},
support = {U19 NS104649/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Learning/physiology ; Mice ; *Motor Cortex/physiology ; Motor Neurons/physiology ; Pyramidal Tracts/physiology ; },
abstract = {During motor learning,1 as well as during neuroprosthetic learning,2-4 animals learn to control motor cortex activity in order to generate behavior. Two different populations of motor cortex neurons, intra-telencephalic (IT) and pyramidal tract (PT) neurons, convey the resulting cortical signals within and outside the telencephalon. Although a large amount of evidence demonstrates contrasting functional organization among both populations,5,6 it is unclear whether the brain can equally learn to control the activity of either class of motor cortex neurons. To answer this question, we used a calcium-imaging-based brain-machine interface (CaBMI)3 and trained different groups of mice to modulate the activity of either IT or PT neurons in order to receive a reward. We found that the animals learned to control PT neuron activity faster and better than IT neuron activity. Moreover, our findings show that the advantage of PT neurons is the result of characteristics inherent to this population as well as their local circuitry and cortical depth location. Taken together, our results suggest that the motor cortex is more efficient at controlling the activity of pyramidal tract neurons, which are embedded deep in the cortex, and relaying motor commands outside the telencephalon.},
}
@article {pmid35214576,
year = {2022},
author = {Usama, N and Niazi, IK and Dremstrup, K and Jochumsen, M},
title = {Single-Trial Classification of Error-Related Potentials in People with Motor Disabilities: A Study in Cerebral Palsy, Stroke, and Amputees.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {4},
pages = {},
pmid = {35214576},
issn = {1424-8220},
support = {22357//VELUX FONDEN/ ; },
mesh = {*Amputees ; Brain ; *Brain-Computer Interfaces ; *Cerebral Palsy ; Electroencephalography ; Humans ; *Stroke/diagnosis ; },
abstract = {Brain-computer interface performance may be reduced over time, but adapting the classifier could reduce this problem. Error-related potentials (ErrPs) could label data for continuous adaptation. However, this has scarcely been investigated in populations with severe motor impairments. The aim of this study was to detect ErrPs from single-trial EEG in offline analysis in participants with cerebral palsy, an amputation, or stroke, and determine how much discriminative information different brain regions hold. Ten participants with cerebral palsy, eight with an amputation, and 25 with a stroke attempted to perform 300-400 wrist and ankle movements while a sham BCI provided feedback on their performance for eliciting ErrPs. Pre-processed EEG epochs were inputted in a multi-layer perceptron artificial neural network. Each brain region was used as input individually (Frontal, Central, Temporal Right, Temporal Left, Parietal, and Occipital), the combination of the Central region with each of the adjacent regions, and all regions combined. The Frontal and Central regions were most important, and adding additional regions only improved performance slightly. The average classification accuracies were 84 ± 4%, 87± 4%, and 85 ± 3% for cerebral palsy, amputation, and stroke participants. In conclusion, ErrPs can be detected in participants with motor impairments; this may have implications for developing adaptive BCIs or automatic error correction.},
}
@article {pmid35214468,
year = {2022},
author = {Mandekar, S and Holland, A and Thielen, M and Behbahani, M and Melnykowycz, M},
title = {Advancing towards Ubiquitous EEG, Correlation of In-Ear EEG with Forehead EEG.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {4},
pages = {},
pmid = {35214468},
issn = {1424-8220},
mesh = {Ear ; Electrodes ; *Electroencephalography/methods ; *Forehead ; Humans ; Scalp ; },
abstract = {Wearable EEG has gained popularity in recent years driven by promising uses outside of clinics and research. The ubiquitous application of continuous EEG requires unobtrusive form-factors that are easily acceptable by the end-users. In this progression, wearable EEG systems have been moving from full scalp to forehead and recently to the ear. The aim of this study is to demonstrate that emerging ear-EEG provides similar impedance and signal properties as established forehead EEG. EEG data using eyes-open and closed alpha paradigm were acquired from ten healthy subjects using generic earpieces fitted with three custom-made electrodes and a forehead electrode (at Fpx) after impedance analysis. Inter-subject variability in in-ear electrode impedance ranged from 20 kΩ to 25 kΩ at 10 Hz. Signal quality was comparable with an SNR of 6 for in-ear and 8 for forehead electrodes. Alpha attenuation was significant during the eyes-open condition in all in-ear electrodes, and it followed the structure of power spectral density plots of forehead electrodes, with the Pearson correlation coefficient of 0.92 between in-ear locations ELE (Left Ear Superior) and ERE (Right Ear Superior) and forehead locations, Fp1 and Fp2, respectively. The results indicate that in-ear EEG is an unobtrusive alternative in terms of impedance, signal properties and information content to established forehead EEG.},
}
@article {pmid35214341,
year = {2022},
author = {Siribunyaphat, N and Punsawad, Y},
title = {Steady-State Visual Evoked Potential-Based Brain-Computer Interface Using a Novel Visual Stimulus with Quick Response (QR) Code Pattern.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {4},
pages = {},
pmid = {35214341},
issn = {1424-8220},
support = {//Walailak University/ ; },
mesh = {Algorithms ; *Asthenopia ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Photic Stimulation/methods ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems suffer from low SSVEP response intensity and visual fatigue, resulting in lower accuracy when operating the system for continuous commands, such as an electric wheelchair control. This study proposes two SSVEP improvements to create a practical BCI for communication and control in disabled people. The first is flicker pattern modification for increasing SSVEP response through mixing (1) fundamental and first harmonic frequencies, and (2) two fundamental frequencies for an additional number of commands. The second method utilizes a quick response (QR) code for visual stimulus patterns to increase the SSVEP response and reduce visual fatigue. Eight different stimulus patterns from three flickering frequencies (7, 13, and 17 Hz) were presented to twelve participants for the test and score levels of visual fatigue. Two popular SSVEP methods, i.e., power spectral density (PSD) with Welch periodogram and canonical correlation analysis (CCA) with overlapping sliding window, are used to detect SSVEP intensity and response, compared to the checkerboard pattern. The results suggest that the QR code patterns can yield higher accuracy than checkerboard patterns for both PSD and CCA methods. Moreover, a QR code pattern with low frequency can reduce visual fatigue; however, visual fatigue can be easily affected by high flickering frequency. The findings can be used in the future to implement a real-time, SSVEP-based BCI for verifying user and system performance in actual environments.},
}
@article {pmid35210460,
year = {2022},
author = {Khalil, K and Asgher, U and Ayaz, Y},
title = {Novel fNIRS study on homogeneous symmetric feature-based transfer learning for brain-computer interface.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {3198},
pmid = {35210460},
issn = {2045-2322},
abstract = {The brain-computer interface (BCI) provides an alternate means of communication between the brain and external devices by recognizing the brain activities and translating them into external commands. The functional Near-Infrared Spectroscopy (fNIRS) is becoming popular as a non-invasive modality for brain activity detection. The recent trends show that deep learning has significantly enhanced the performance of the BCI systems. But the inherent bottleneck for deep learning (in the domain of BCI) is the requirement of the vast amount of training data, lengthy recalibrating time, and expensive computational resources for training deep networks. Building a high-quality, large-scale annotated dataset for deep learning-based BCI systems is exceptionally tedious, complex, and expensive. This study investigates the novel application of transfer learning for fNIRS-based BCI to solve three objective functions (concerns), i.e., the problem of insufficient training data, reduced training time, and increased accuracy. We applied symmetric homogeneous feature-based transfer learning on convolutional neural network (CNN) designed explicitly for fNIRS data collected from twenty-six (26) participants performing the n-back task. The results suggested that the proposed method achieves the maximum saturated accuracy sooner and outperformed the traditional CNN model on averaged accuracy by 25.58% in the exact duration of training time, reducing the training time, recalibrating time, and computational resources.},
}
@article {pmid35209753,
year = {2022},
author = {Damigos, G and Zacharaki, EI and Zerva, N and Pavlopoulos, A and Chatzikyrkou, K and Koumenti, A and Moustakas, K and Pantos, C and Mourouzis, I and Lourbopoulos, A},
title = {Machine learning based analysis of stroke lesions on mouse tissue sections.},
journal = {Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism},
volume = {},
number = {},
pages = {271678X221083387},
doi = {10.1177/0271678X221083387},
pmid = {35209753},
issn = {1559-7016},
abstract = {An unbiased, automated and reliable method for analysis of brain lesions in tissue after ischemic stroke is missing. Manual infarct volumetry or by threshold-based semi-automated approaches is laborious, and biased to human error or biased by many false -positive and -negative data, respectively. Thereby, we developed a novel machine learning, atlas-based method for fully automated stroke analysis in mouse brain slices stained with 2% Triphenyltetrazolium-chloride (2% TTC), named "StrokeAnalyst", which runs on a user-friendly graphical interface. StrokeAnalyst registers subject images on a common spatial domain (a novel mouse TTC- brain atlas of 80 average mathematical images), calculates pixel-based, tissue-intensity statistics (z-scores), applies outlier-detection and machine learning (Random-Forest) models to increase accuracy of lesion detection, and produces volumetry data and detailed neuroanatomical information per lesion. We validated StrokeAnalyst in two separate experimental sets using the filament stroke model. StrokeAnalyst detects stroke lesions in a rater-independent and reproducible way, correctly detects hemispheric volumes even in presence of post-stroke edema and significantly minimizes false-positive errors compared to threshold-based approaches (false-positive rate 1.2-2.3%, p < 0.05). It can process scanner-acquired, and even smartphone-captured or pdf-retrieved images. Overall, StrokeAnalyst surpasses all previous TTC-volumetry approaches and increases quality, reproducibility and reliability of stroke detection in relevant preclinical models.},
}
@article {pmid35208323,
year = {2022},
author = {Wang, M and Fan, Y and Li, L and Wen, F and Guo, B and Jin, M and Xu, J and Zhou, Y and Kang, X and Ji, B and Cheng, Y and Wang, G},
title = {Flexible Neural Probes with Optical Artifact-Suppressing Modification and Biofriendly Polypeptide Coating.},
journal = {Micromachines},
volume = {13},
number = {2},
pages = {},
pmid = {35208323},
issn = {2072-666X},
support = {2018YFE0120000//National Key R&amp;D Program of China/ ; GK199900X001//Fundamental Research Funds for the Provincial Universities of Zhejiang/ ; LQ21F010010//Zhejiang Provincial Natural Science Foundation of China/ ; 2019C04003//Zhejiang Provincial Key Research &amp; Development Project/ ; 62141409//National Natural Science Foundation of China/ ; 62104056//National Natural Science Foundation of China/ ; 2020TQ0246, 2021M692638//China Postdoctoral Science Foundation/ ; 21YF1451000//Shanghai Sailing Program/ ; 31020200QD013//the Fundamental Research Funds for the Central Universities/ ; },
abstract = {The advent of optogenetics provides a well-targeted tool to manipulate neurons because of its high time resolution and cell-type specificity. Recently, closed-loop neural manipulation techniques consisting of optical stimulation and electrical recording have been widely used. However, metal microelectrodes exposed to light radiation could generate photoelectric noise, thus causing loss or distortion of neural signal in recording channels. Meanwhile, the biocompatibility of neural probes remains to be improved. Here, five kinds of neural interface materials are deposited on flexible polyimide-based neural probes and illuminated with a series of blue laser pulses to study their electrochemical performance and photoelectric noises for single-unit recording. The results show that the modifications can not only improve the electrochemical performance, but can also reduce the photoelectric artifacts. In particular, the double-layer composite consisting of platinum-black and conductive polymer has the best comprehensive performance. Thus, a layer of polypeptide is deposited on the entire surface of the double-layer modified neural probes to further improve their biocompatibility. The results show that the biocompatible polypeptide coating has little effect on the electrochemical performance of the neural probe, and it may serve as a drug carrier due to its special micromorphology.},
}
@article {pmid35206341,
year = {2022},
author = {Jung, D and Choi, J and Kim, J and Cho, S and Han, S},
title = {EEG-Based Identification of Emotional Neural State Evoked by Virtual Environment Interaction.},
journal = {International journal of environmental research and public health},
volume = {19},
number = {4},
pages = {},
pmid = {35206341},
issn = {1660-4601},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Emotions/physiology ; *Gene-Environment Interaction ; Humans ; Support Vector Machine ; },
abstract = {Classifying emotional states is critical for brain-computer interfaces and psychology-related domains. In previous studies, researchers have tried to identify emotions using neural data such as electroencephalography (EEG) signals or brain functional magnetic resonance imaging (fMRI). In this study, we propose a machine learning framework for emotion state classification using EEG signals in virtual reality (VR) environments. To arouse emotional neural states in brain signals, we provided three VR stimuli scenarios to 15 participants. Fifty-four features were extracted from the collected EEG signals under each scenario. To find the optimal classification in our research design, three machine learning algorithms (XGBoost classifier, support vector classifier, and logistic regression) were applied. Additionally, various class conditions were used in machine learning classifiers to validate the performance of our framework. To evaluate the classification performance, we utilized five evaluation metrics (precision, recall, f1-score, accuracy, and AUROC). Among the three classifiers, the XGBoost classifiers showed the best performance under all experimental conditions. Furthermore, the usability of features, including differential asymmetry and frequency band pass categories, were checked from the feature importance of XGBoost classifiers. We expect that our framework can be applied widely not only to psychological research but also to mental health-related issues.},
}
@article {pmid35205490,
year = {2022},
author = {Jiang, L and Liu, S and Ma, Z and Lei, W and Chen, C},
title = {Regularized RKHS-Based Subspace Learning for Motor Imagery Classification.},
journal = {Entropy (Basel, Switzerland)},
volume = {24},
number = {2},
pages = {},
pmid = {35205490},
issn = {1099-4300},
support = {61773022//National Natural Science Foundation of China/ ; 68000-42050001//Science and Technology Program of Guangzhou/ ; },
abstract = {Brain-computer interface (BCI) technology allows people with disabilities to communicate with the physical environment. One of the most promising signals is the non-invasive electroencephalogram (EEG) signal. However, due to the non-stationary nature of EEGs, a subject's signal may change over time, which poses a challenge for models that work across time. Recently, domain adaptive learning (DAL) has shown its superior performance in various classification tasks. In this paper, we propose a regularized reproducing kernel Hilbert space (RKHS) subspace learning algorithm with K-nearest neighbors (KNNs) as a classifier for the task of motion imagery signal classification. First, we reformulate the framework of RKHS subspace learning with a rigorous mathematical inference. Secondly, since the commonly used maximum mean difference (MMD) criterion measures the distribution variance based on the mean value only and ignores the local information of the distribution, a regularization term of source domain linear discriminant analysis (SLDA) is proposed for the first time, which reduces the variance of similar data and increases the variance of dissimilar data to optimize the distribution of source domain data. Finally, the RKHS subspace framework was constructed sparsely considering the sensitivity of the BCI data. We test the proposed algorithm in this paper, first on four standard datasets, and the experimental results show that the other baseline algorithms improve the average accuracy by 2-9% after adding SLDA. In the motion imagery classification experiments, the average accuracy of our algorithm is 3% higher than the other algorithms, demonstrating the adaptability and effectiveness of the proposed algorithm.},
}
@article {pmid35204415,
year = {2022},
author = {Jana, GC and Agrawal, A and Pattnaik, PK and Sain, M},
title = {DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {12},
number = {2},
pages = {},
pmid = {35204415},
issn = {2075-4418},
support = {Research Fund of 2021 (DSU-20210004).//This work was supported by Dongseo University, "Dongseo Cluster Project"/ ; },
abstract = {Brain Computer Interface technology enables a pathway for analyzing EEG signals for seizure detection. EEG signal decomposition, features extraction and machine learning techniques are more familiar in seizure detection. However, selecting decomposition technique and concatenation of their features for seizure detection is still in the state-of-the-art phase. This work proposes DWT-EMD Feature level Fusion-based seizure detection approach over multi and single channel EEG signals and studied the usability of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) feature fusion with respect to individual DWT and EMD features over classifiers SVM, SVM with RBF kernel, decision tree and bagging classifier for seizure detection. All classifiers achieved an improved performance over DWT-EMD feature level fusion for two benchmark seizure detection EEG datasets. Detailed quantification results have been mentioned in the Results section.},
}
@article {pmid35204011,
year = {2022},
author = {Quiles, V and Ferrero, L and Iáñez, E and Ortiz, M and Azorín, JM},
title = {Review of tDCS Configurations for Stimulation of the Lower-Limb Area of Motor Cortex and Cerebellum.},
journal = {Brain sciences},
volume = {12},
number = {2},
pages = {},
pmid = {35204011},
issn = {2076-3425},
support = {RTI2018-096677-B-I00//MCIN/AEI/10.13039/501100011033/ ; },
abstract = {This article presents an exhaustive analysis of the works present in the literature pertaining to transcranial direct current stimulation(tDCS) applications. The aim of this work is to analyze the specific characteristics of lower-limb stimulation, identifying the strengths and weaknesses of these works and framing them with the current knowledge of tDCS. The ultimate goal of this work is to propose areas of improvement to create more effective stimulation therapies with less variability.},
}
@article {pmid35203998,
year = {2022},
author = {Stawicki, P and Volosyak, I},
title = {cVEP Training Data Validation-Towards Optimal Training Set Composition from Multi-Day Data.},
journal = {Brain sciences},
volume = {12},
number = {2},
pages = {},
pmid = {35203998},
issn = {2076-3425},
abstract = {This paper investigates the effects of the repetitive block-wise training process on the classification accuracy for a code-modulated visual evoked potentials (cVEP)-based brain-computer interface (BCI). The cVEP-based BCIs are popular thanks to their autocorrelation feature. The cVEP-based stimuli are generated by a specific code pattern, usually the m-sequence, which is phase-shifted between the individual targets. Typically, the cVEP classification requires a subject-specific template (individually created from the user's own pre-recorded EEG responses to the same stimulus target), which is compared to the incoming electroencephalography (EEG) data, using the correlation algorithms. The amount of the collected user training data determines the accuracy of the system. In this offline study, previously recorded EEG data collected during an online experiment with 10 participants from multiple sessions were used. A template matching target identification, with similar models as the task-related component analysis (TRCA), was used for target classification. The spatial filter was generated by the canonical correlation analysis (CCA). When comparing the training models from one session with the same session's data (intra-session) and the model from one session with the data from the other session (inter-session), the accuracies were (94.84%, 94.53%) and (76.67%, 77.34%) for intra-sessions and inter-sessions, respectively. In order to investigate the most reliable configuration for accurate classification, the training data blocks from different sessions (days) were compared interchangeably. In the best training set composition, the participants achieved an average accuracy of 82.66% for models based only on two training blocks from two different sessions. Similarly, at least five blocks were necessary for the average accuracy to exceed 90%. The presented method can further improve cVEP-based BCI performance by reusing previously recorded training data.},
}
@article {pmid35203991,
year = {2022},
author = {Du, B and Cheng, X and Duan, Y and Ning, H},
title = {fMRI Brain Decoding and Its Applications in Brain-Computer Interface: A Survey.},
journal = {Brain sciences},
volume = {12},
number = {2},
pages = {},
pmid = {35203991},
issn = {2076-3425},
support = {KC2019SZ11, 54896055//University of Science and Technology Course Fund/ ; U1633121//the National Nature Science Foundation of China/ ; },
abstract = {Brain neural activity decoding is an important branch of neuroscience research and a key technology for the brain-computer interface (BCI). Researchers initially developed simple linear models and machine learning algorithms to classify and recognize brain activities. With the great success of deep learning on image recognition and generation, deep neural networks (DNN) have been engaged in reconstructing visual stimuli from human brain activity via functional magnetic resonance imaging (fMRI). In this paper, we reviewed the brain activity decoding models based on machine learning and deep learning algorithms. Specifically, we focused on current brain activity decoding models with high attention: variational auto-encoder (VAE), generative confrontation network (GAN), and the graph convolutional network (GCN). Furthermore, brain neural-activity-decoding-enabled fMRI-based BCI applications in mental and psychological disease treatment are presented to illustrate the positive correlation between brain decoding and BCI. Finally, existing challenges and future research directions are addressed.},
}
@article {pmid35203185,
year = {2022},
author = {Niu, X and Huang, S and Zhu, M and Wang, Z and Shi, L},
title = {Surround Modulation Properties of Tectal Neurons in Pigeons Characterized by Moving and Flashed Stimuli.},
journal = {Animals : an open access journal from MDPI},
volume = {12},
number = {4},
pages = {},
pmid = {35203185},
issn = {2076-2615},
support = {61673353//National Natural Science Foundation of China/ ; 20A413009//Key Scientific Research Projects of Colleges and Universities in Henan province/ ; XKZDQY201905//the Key Discipline Construction Project of Zhengzhou University in 2019/ ; },
abstract = {Surround modulation has been abundantly studied in several mammalian brain areas, including the primary visual cortex, lateral geniculate nucleus, and superior colliculus (SC), but systematic analysis is lacking in the avian optic tectum (OT, homologous to mammal SC). Here, multi-units were recorded from pigeon (Columba livia) OT, and responses to different sizes of moving, flashed squares, and bars were compared. The statistical results showed that most tectal neurons presented suppressed responses to larger stimuli in both moving and flashed paradigms, and suppression induced by flashed squares was comparable with moving ones when the stimuli center crossed the near classical receptive field (CRF) center, which corresponded to the full surrounding condition. Correspondingly, the suppression grew weaker when the stimuli center moved across the CRF border, equivalent to partially surrounding conditions. Similarly, suppression induced by full surrounding flashed squares was more intense than by partially surrounding flashed bars. These results suggest that inhibitions performed on tectal neurons appear to be full surrounding rather than locally lateral. This study enriches the understanding of surround modulation properties of avian tectum neurons and provides possible hypotheses about the arrangement of inhibitions from other nuclei, both of which are important for clarifying the mechanism of target detection against clutter background performed by avians.},
}
@article {pmid35202615,
year = {2022},
author = {Han, J and Liu, C and Chu, J and Xiao, X and Chen, L and Xu, M and Ming, D},
title = {Effects of inter-stimulus intervals on concurrent P300 and SSVEP features for hybrid brain-computer interfaces.},
journal = {Journal of neuroscience methods},
volume = {372},
number = {},
pages = {109535},
doi = {10.1016/j.jneumeth.2022.109535},
pmid = {35202615},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Recently, we have implemented a high-speed brain-computer interface (BCI) system with a large instruction set using the concurrent P300 and steady-state visual evoked potential (SSVEP) features (also known as hybrid features). However, it remains unclear how to select inter-stimulus interval (ISI) for the proposed BCI system to balance the encoding efficiency and decoding performance.<br><br>NEW METHOD: This study developed a 6 * 9 hybrid P300-SSVEP BCI system and investigated a series of ISIs ranged from -175-0 ms with a step of 25 ms. The influence of ISI on the hybrid features was analyzed from several aspects, including the amplitude of the induced features, classification accuracy, information transfer rate (ITR). Twelve naive subjects were recruited for the experiment.<br><br>RESULTS: The results showed the ISI factor had a significant impact on the hybrid features. Specifically, as the values of ISI decreased, the amplitudes of the induced features and accuracies decreased gradually, while the ITRs increased rapidly. It's achieved the highest ITR of 158.50 bits/min when ISI equal to - 175 ms.<br><br>The optimal ISI in this study achieved superior performance in comparison with the one we used in the previous study.<br><br>CONCLUSIONS: The ISI can exert an important influence on the P300-SSVEP BCI system and its optimal value is - 175 ms in this study, which is significant for developing the high-speed BCI system with larger instruction sets in the future.},
}
@article {pmid35201988,
year = {2022},
author = {Ma, X and Qiu, S and He, H},
title = {Time-Distributed Attention Network for EEG-Based Motor Imagery Decoding From the Same Limb.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {496-508},
doi = {10.1109/TNSRE.2022.3154369},
pmid = {35201988},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagination/physiology ; Upper Extremity ; },
abstract = {A brain-computer interface (BCI) based on motor imagery (MI) from the same limb can provide an intuitive control pathway but has received limited attention. It is still a challenge to classify multiple MI tasks from the same limb. The goal of this study is to propose a novel decoding method to classify the MI tasks of four joints of the same upper limb and the resting state. EEG signals were collected from 20 participants. A time-distributed attention network (TD-Atten) was proposed to adaptively assign different weights to different classes and frequency bands of the input multiband Common Spatial Pattern (CSP) features. The long short-term memory (LSTM) and dense layers were then used to learn sequential information from the reweight features and perform the classification. Our proposed method outperformed other baseline and deep learning-based methods and obtained the accuracies of 46.8% in the 5-class scenario and 53.4% in the 4-class scenario. The visualization results of attention weights indicated that the proposed framework can adaptively pay attention to alpha-band related features in MI tasks, which was consistent with the analysis of brain activation patterns. These results demonstrated the feasibility and interpretability of the attention mechanism in MI decoding and the potential of this fine MI paradigm to be applied for the control of a robotic arm or a neural prosthesis.},
}
@article {pmid35200389,
year = {2022},
author = {Yang, N and Liu, F and Zhang, X and Chen, C and Xia, Z and Fu, S and Wang, J and Xu, J and Cui, S and Zhang, Y and Yi, M and Wan, Y and Li, Q and Xu, S},
title = {A Hybrid Titanium-Softmaterial, High-Strength, Transparent Cranial Window for Transcranial Injection and Neuroimaging.},
journal = {Biosensors},
volume = {12},
number = {2},
pages = {},
pmid = {35200389},
issn = {2079-6374},
support = {2017YFA0701302//National Key R&amp;D Program of China/ ; 32171002//National Natural Science Foundation of China/ ; 81974166//National Natural Science Foundation of China/ ; 31872774//National Natural Science Foundation of China/ ; BMU2021MX002//Interdisciplinary Medicine Seed Fund of Peking University/ ; },
mesh = {Animals ; Mice ; Neuroimaging/methods ; Photons ; Printing, Three-Dimensional/instrumentation ; *Skull/diagnostic imaging ; *Titanium ; },
abstract = {A transparent and penetrable cranial window is essential for neuroimaging, transcranial injection and comprehensive understanding of cortical functions. For these applications, cranial windows made from glass coverslip, polydimethylsiloxane (PDMS), polymethylmethacrylate, crystal and silicone hydrogel have offered remarkable convenience. However, there is a lack of high-strength, high-transparency, penetrable cranial window with clinical application potential. We engineer high-strength hybrid Titanium-PDMS (Ti-PDMS) cranial windows, which allow large transparent area for in vivo two-photon imaging, and provide a soft window for transcranial injection. Laser scanning and 3D printing techniques are used to match the hybrid cranial window to different skull morphology. A multi-cycle degassing pouring process ensures a good combination of PDMS and Ti frame. Ti-PDMS cranial windows have a high fracture strength matching human skull bone, excellent light transmittance up to 94.4%, and refractive index close to biological tissue. Ti-PDMS cranial windows show excellent bio-compatibility during 21-week implantation in mice. Dye injection shows that the PDMS window has a "self-sealing" to keep liquid from leaking out. Two-photon imaging for brain tissues could be achieved up to 450 µm in z-depth. As a novel brain-computer-interface, this Ti-PDMS device offers an alternative choice for in vivo drug delivery, optical experiments, ultrasonic treatment and electrophysiology recording.},
}
@article {pmid35197817,
year = {2021},
author = {Yang, W and Zhang, X and Li, Z and Zhang, Q and Xue, C and Huai, Y},
title = {The Effect of Brain-Computer Interface Training on Rehabilitation of Upper Limb Dysfunction After Stroke: A Meta-Analysis of Randomized Controlled Trials.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {766879},
pmid = {35197817},
issn = {1662-4548},
abstract = {Background: Upper limb motor dysfunction caused by stroke greatly affects the daily life of patients, significantly reduces their quality of life, and places serious burdens on society. As an emerging rehabilitation training method, brain-computer interface (BCI)-based training can provide closed-loop rehabilitation and is currently being applied to the restoration of upper limb function following stroke. However, because of the differences in the type of experimental clinical research, the quality of the literature varies greatly, and debate around the efficacy of BCI for the rehabilitation of upper limb dysfunction after stroke has continued.<br><br>Objective: We aimed to provide medical evidence-based support for BCI in the treatment of upper limb dysfunction after stroke by conducting a meta-analysis of relevant clinical studies.<br><br>Methods: The search terms used to retrieve related articles included "brain-computer interface," "stroke," and "upper extremity." A total of 13 randomized controlled trials involving 258 participants were retrieved from five databases (PubMed, Cochrane Library, Science Direct, MEDLINE, and Web of Science), and RevMan 5.3 was used for data analysis.<br><br>Results: The total effect size for BCI training on upper limb motor function of post-stroke patients was 0.56 (95% CI: 0.29-0.83). Subgroup analysis indicated that the standard mean differences of BCI training on upper limb motor function of subacute stroke patients and chronic stroke patients were 1.10 (95% CI: 0.20-2.01) and 0.51 (95% CI: 0.09-0.92), respectively (p = 0.24).<br><br>Conclusion: Brain-computer interface training was shown to be effective in promoting upper limb motor function recovery in post-stroke patients, and the effect size was moderate.},
}
@article {pmid35196360,
year = {2022},
author = {Halme, HL and Parkkonen, L},
title = {The effect of visual and proprioceptive feedback on sensorimotor rhythms during BCI training.},
journal = {PloS one},
volume = {17},
number = {2},
pages = {e0264354},
pmid = {35196360},
issn = {1932-6203},
mesh = {Adult ; Brain Waves ; Brain-Computer Interfaces ; *Feedback, Sensory ; Humans ; *Proprioception ; Sensorimotor Cortex/*physiology ; *Visual Perception ; },
abstract = {Brain-computer interfaces (BCI) can be designed with several feedback modalities. To promote appropriate brain plasticity in therapeutic applications, the feedback should guide the user to elicit the desired brain activity and preferably be similar to the imagined action. In this study, we employed magnetoencephalography (MEG) to measure neurophysiological changes in healthy subjects performing motor imagery (MI) -based BCI training with two different feedback modalities. The MI-BCI task used in this study lasted 40-60 min and involved imagery of right- or left-hand movements. 8 subjects performed the task with visual and 14 subjects with proprioceptive feedback. We analysed power changes across the session at multiple frequencies in the range of 4-40 Hz with a generalized linear model to find those frequencies at which the power increased significantly during training. In addition, the power increase was analysed for each gradiometer, separately for alpha (8-13 Hz), beta (14-30 Hz) and gamma (30-40 Hz) bands, to find channels showing significant linear power increase over the session. These analyses were applied during three different conditions: rest, preparation, and MI. Visual feedback enhanced the amplitude of mainly high beta and gamma bands (24-40 Hz) in all conditions in occipital and left temporal channels. During proprioceptive feedback, in contrast, power increased mainly in alpha and beta bands. The alpha-band enhancement was found in multiple parietal, occipital, and temporal channels in all conditions, whereas the beta-band increase occurred during rest and preparation mainly in the parieto-occipital region and during MI in the parietal channels above hand motor regions. Our results show that BCI training with proprioceptive feedback increases the power of sensorimotor rhythms in the motor cortex, whereas visual feedback causes mainly a gamma-band increase in the visual cortex. MI-BCIs should involve proprioceptive feedback to facilitate plasticity in the motor cortex.},
}
@article {pmid35195458,
year = {2022},
author = {Zerrouki, F and Haddab, S},
title = {Experimental Validation of the Cumulative MDRM in theP300 Speller Machine.},
journal = {Clinical EEG and neuroscience},
volume = {},
number = {},
pages = {15500594221078166},
doi = {10.1177/15500594221078166},
pmid = {35195458},
issn = {2169-5202},
abstract = {The P300 speller Machine is among the leading applications of the electroencephalography (EEG)-based brain computer interfaces (BCIs), it is still a benchmark and a persistent challenge for the BCI Community. EEG signal classification represents the key piece of a BCI chain. The minimum distance to Riemannian mean (MDRM) belongs to these classification methods emerging in different BCI applications such as text spelling by thought. Based on a binary classification of each covariance matrix separately, character prediction is done according to the highest score across the whole set of all repetitions. Minimum cumulative distance to Riemannian mean (MCDRM) is a Cumulative variant of the MDRM, perfectly adapted to the P300 Speller Machine. The power of this variant is that prediction takes a more global proceeding involving the n repetitions together. Indeed, thanks to cumulative distances selected row and column are those having the covariance matrices both closer to the Target barycenter and farther from the non-Target one. This variant overcomes the main MDRM limitations as it improves inter-sessional generalization, allows optimal use of all repetitions and reduces considerably the risk of conflict appearing during the selection of rows and columns leading to character prediction. We applied this variant to the raw signals of Data set II-b of Berlin BCI and compared to the published results the MCDRM offers significantly higher results: 97.5% of correct predictions compared to the 96.5% of the competition winner. The MCDRM fits best with the P300 Speller machine, especially when dealing with noisy signals that requires intelligent and optimal usage of the n repetitions.},
}
@article {pmid35194026,
year = {2022},
author = {Harikesh, PC and Yang, CY and Tu, D and Gerasimov, JY and Dar, AM and Armada-Moreira, A and Massetti, M and Kroon, R and Bliman, D and Olsson, R and Stavrinidou, E and Berggren, M and Fabiano, S},
title = {Organic electrochemical neurons and synapses with ion mediated spiking.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {901},
pmid = {35194026},
issn = {2041-1723},
mesh = {*Brain-Computer Interfaces ; Neuronal Plasticity ; Neurons ; *Robotics ; Silicon ; Synapses/physiology ; },
abstract = {Future brain-machine interfaces, prosthetics, and intelligent soft robotics will require integrating artificial neuromorphic devices with biological systems. Due to their poor biocompatibility, circuit complexity, low energy efficiency, and operating principles fundamentally different from the ion signal modulation of biology, traditional Silicon-based neuromorphic implementations have limited bio-integration potential. Here, we report the first organic electrochemical neurons (OECNs) with ion-modulated spiking, based on all-printed complementary organic electrochemical transistors. We demonstrate facile bio-integration of OECNs with Venus Flytrap (Dionaea muscipula) to induce lobe closure upon input stimuli. The OECNs can also be integrated with all-printed organic electrochemical synapses (OECSs), exhibiting short-term plasticity with paired-pulse facilitation and long-term plasticity with retention >1000 s, facilitating Hebbian learning. These soft and flexible OECNs operate below 0.6 V and respond to multiple stimuli, defining a new vista for localized artificial neuronal systems possible to integrate with bio-signaling systems of plants, invertebrates, and vertebrates.},
}
@article {pmid35191214,
year = {2022},
author = {Chalmers, T and Eaves, S and Lees, T and Lin, CT and Newton, PJ and Clifton-Bligh, R and McLachlan, CS and Gustin, SM and Lal, S},
title = {The relationship between neurocognitive performance and HRV parameters in nurses and non-healthcare participants.},
journal = {Brain and behavior},
volume = {12},
number = {3},
pages = {e2481},
pmid = {35191214},
issn = {2162-3279},
abstract = {Nurses represent the largest sector of the healthcare workforce, and it is established that they are faced with ongoing physical and mental demands that leave many continuously stressed. In turn, this chronic stress may affect cardiac autonomic activity, which can be non-invasively evaluated using heart rate variability (HRV). The association between neurocognitive parameters during acute stress situations and HRV has not been previously explored in nurses compared to non-nurses and such, our study aimed to assess these differences. Neurocognitive data were obtained using the Mini-Mental State Examination and Cognistat psychometric questionnaires. ECG-derived HRV parameters were acquired during the Trier Social Stress Test. Between-group differences were found in domain-specific cognitive performance for the similarities (p = .03), and judgment (p = .002) domains and in the following HRV parameters: SDNNbaseline, (p = .004), LFpreparation (p = .002), SDNNpreparation (p = .002), HFpreparation (p = .02), and TPpreparation (p = .003). Negative correlations were found between HF power and domain-specific cognitive performance in nurses. In contrast, both negative and positive correlations were found between HRV and domain-specific cognitive performance in the non-nurse group. The current findings highlight the prospective use of autonomic HRV markers in relation to cognitive performance while building a relationship between autonomic dysfunction and cognition.},
}
@article {pmid35185500,
year = {2022},
author = {Jiang, L and Li, X and Pei, W and Gao, X and Wang, Y},
title = {A Hybrid Brain-Computer Interface Based on Visual Evoked Potential and Pupillary Response.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {834959},
pmid = {35185500},
issn = {1662-5161},
abstract = {Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has been widely studied due to the high information transfer rate (ITR), little user training, and wide subject applicability. However, there are also disadvantages such as visual discomfort and "BCI illiteracy." To address these problems, this study proposes to use low-frequency stimulations (12 classes, 0.8-2.12 Hz with an interval of 0.12 Hz), which can simultaneously elicit visual evoked potential (VEP) and pupillary response (PR) to construct a hybrid BCI (h-BCI) system. Classification accuracy was calculated using supervised and unsupervised methods, respectively, and the hybrid accuracy was obtained using a decision fusion method to combine the information of VEP and PR. Online experimental results from 10 subjects showed that the averaged accuracy was 94.90 ± 2.34% (data length 1.5 s) for the supervised method and 91.88 ± 3.68% (data length 4 s) for the unsupervised method, which correspond to the ITR of 64.35 ± 3.07 bits/min (bpm) and 33.19 ± 2.38 bpm, respectively. Notably, the hybrid method achieved higher accuracy and ITR than that of VEP and PR for most subjects, especially for the short data length. Together with the subjects' feedback on user experience, these results indicate that the proposed h-BCI with the low-frequency stimulation paradigm is more comfortable and favorable than the traditional SSVEP-BCI paradigm using the alpha frequency range.},
}
@article {pmid35182185,
year = {2022},
author = {Cywka, KB and Skarzynski, PH and Krol, B and Hatzopoulos, S and Skarzynski, H},
title = {Evaluation of the Bonebridge BCI 602 active bone conductive implant in adults: efficacy and stability of audiological, surgical, and functional outcomes.},
journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery},
volume = {},
number = {},
pages = {},
pmid = {35182185},
issn = {1434-4726},
abstract = {PURPOSE: (1) To assess the effectiveness and safety of a bone-conduction implant, the Bonebridge BCI 602, in adults with conductive or mixed hearing loss. (2) To investigate whether the Bonebridge BCI 602 is at least as effective as the Bonebridge BCI 601 in such patients.<br><br>METHODS: The study group included 42 adults who had either conductive or mixed hearing loss. All patients underwent Bonebridge BCI 602 implant surgery. Before and after implantation, pure-tone audiometry, speech recognition tests (in quiet and noise), and free-field audiometry were performed. Word recognition scores were evaluated using the Polish Monosyllabic Word Test. Speech reception thresholds in noise were assessed using the Polish Sentence Matrix Test. Subjective assessment of benefits was done using the APHAB (Abbreviated Profile of Hearing Aid Benefit) questionnaire.<br><br>RESULTS: The APHAB questionnaire showed that difficulties in hearing decreased after BCI 602 implantation. Both word recognition in quiet and speech reception threshold in noise were significantly better after BCI 602 implantation and remained stable for at least 12 months. A significant advantage of the device is a reduced time for surgery while maintaining safety. In this study, the mean time for BCI 602 implantation was 28.3 min ± 9.4.<br><br>CONCLUSIONS: The second-generation Bonebridge BCI 602 implant is an effective hearing rehabilitation device for patients with conductive or mixed hearing loss. Patient satisfaction and audiological results confirm its efficacy and safety. Its new shape and dimensions allow it to be used in patients previously excluded due to insufficient or difficult anatomical conditions. The new BCI 602 implant is as effective as its predecessor, the BCI 601.},
}
@article {pmid35181656,
year = {2022},
author = {Orlando, G and Raimondi, D and Duran-Romaña, R and Moreau, Y and Schymkowitz, J and Rousseau, F},
title = {PyUUL provides an interface between biological structures and deep learning algorithms.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {961},
pmid = {35181656},
issn = {2041-1723},
mesh = {Algorithms ; Computational Biology/*methods ; *Deep Learning ; Humans ; Imaging, Three-Dimensional/*methods ; *Neural Networks, Computer ; Protein Structural Elements/physiology ; },
abstract = {Structural bioinformatics suffers from the lack of interfaces connecting biological structures and machine learning methods, making the application of modern neural network architectures impractical. This negatively affects the development of structure-based bioinformatics methods, causing a bottleneck in biological research. Here we present PyUUL (https://pyuul.readthedocs.io/), a library to translate biological structures into 3D tensors, allowing an out-of-the-box application of state-of-the-art deep learning algorithms. The library converts biological macromolecules to data structures typical of computer vision, such as voxels and point clouds, for which extensive machine learning research has been performed. Moreover, PyUUL allows an out-of-the box GPU and sparse calculation. Finally, we demonstrate how PyUUL can be used by researchers to address some typical bioinformatics problems, such as structure recognition and docking.},
}
@article {pmid35181552,
year = {2022},
author = {Banville, H and Wood, SUN and Aimone, C and Engemann, DA and Gramfort, A},
title = {Robust learning from corrupted EEG with dynamic spatial filtering.},
journal = {NeuroImage},
volume = {251},
number = {},
pages = {118994},
doi = {10.1016/j.neuroimage.2022.118994},
pmid = {35181552},
issn = {1095-9572},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Machine Learning ; Neural Networks, Computer ; },
abstract = {Building machine learning models using EEG recorded outside of the laboratory setting requires methods robust to noisy data and randomly missing channels. This need is particularly great when working with sparse EEG montages (1-6 channels), often encountered in consumer-grade or mobile EEG devices. Neither classical machine learning models nor deep neural networks trained end-to-end on EEG are typically designed or tested for robustness to corruption, and especially to randomly missing channels. While some studies have proposed strategies for using data with missing channels, these approaches are not practical when sparse montages are used and computing power is limited (e.g., wearables, cell phones). To tackle this problem, we propose dynamic spatial filtering (DSF), a multi-head attention module that can be plugged in before the first layer of a neural network to handle missing EEG channels by learning to focus on good channels and to ignore bad ones. We tested DSF on public EEG data encompassing ∼4000 recordings with simulated channel corruption and on a private dataset of ∼100 at-home recordings of mobile EEG with natural corruption. Our proposed approach achieves the same performance as baseline models when no noise is applied, but outperforms baselines by as much as 29.4% accuracy when significant channel corruption is present. Moreover, DSF outputs are interpretable, making it possible to monitor the effective channel importance in real-time. This approach has the potential to enable the analysis of EEG in challenging settings where channel corruption hampers the reading of brain signals.},
}
@article {pmid35181199,
year = {2022},
author = {Kasprzyk-Hordern, B and Proctor, K and Jagadeesan, K and Edler, F and Standerwick, R and Barden, R},
title = {Human population as a key driver of biochemical burden in an inter-city system: Implications for One Health concept.},
journal = {Journal of hazardous materials},
volume = {429},
number = {},
pages = {127882},
doi = {10.1016/j.jhazmat.2021.127882},
pmid = {35181199},
issn = {1873-3336},
mesh = {Cities ; Drug Resistance, Microbial ; Humans ; *One Health ; *Pesticides/analysis ; Waste Water/analysis ; },
abstract = {This paper tests the hypothesis that human population and city function are key drivers of biochemical burden in an inter-city system, which can be used to inform One Health actions as it enables a holistic understanding of city's metabolism encompassing all of the activities of a city in a single model: from lifestyle choices, through to health status and exposure to harmful chemicals as well as effectiveness of implemented management strategies. Chemical mining of wastewater for biophysico-chemical indicators (BCIs) was undertaken to understand speciation of BCIs in the context of geographical as well as community-wide socioeconomic factors. Spatiotemporal variabilities in chemical and biological target groups in the studied inter-city system were observed. A linear relationship (R2 > 0.99) and a strong positive correlation between most BCIs and population size (r > 0.998, p < 0.001) were observed which provides a strong evidence for the population size as a driver of BCI burden. BCI groups that are strongly correlated with population size and are intrinsic to humans' function include mostly high usage pharmaceuticals that are linked with long term non-communicable conditions (NSAIDs, analgesics, cardiovascular, mental health and antiepileptics) and lifestyle chemicals. These BCIs can be used as population size markers. BCIs groups that are produced as a result of a specific city's function (e.g. industry presence and occupational exposure or agriculture) and as such are not correlated with population size include: pesticides, PCPs and industrial chemicals. These BCIs can be used to assess city's function, such as occupational exposure, environmental or food exposure, and as a proxy of community-wide health. This study confirmed a strong positive correlation between antibiotics (ABs), population size and antibiotic resistance genes (ARGs). This confirms the population size and AB usage as the main driver of AB and ARG levels and provides an opportunity for interventions aimed at the reduction of AB usage to reduce AMR. Holistic evaluation of biophysicochemical fingerprints (BCI burden) of the environment and data triangulation with socioeconomic fingerprints (indices) of tested communities are required to fully embrace One Health concept.},
}
@article {pmid35178518,
year = {2022},
author = {Musso, M and Hübner, D and Schwarzkopf, S and Bernodusson, M and LeVan, P and Weiller, C and Tangermann, M},
title = {Aphasia recovery by language training using a brain-computer interface: a proof-of-concept study.},
journal = {Brain communications},
volume = {4},
number = {1},
pages = {fcac008},
pmid = {35178518},
issn = {2632-1297},
abstract = {Aphasia, the impairment to understand or produce language, is a frequent disorder after stroke with devastating effects. Conventional speech and language therapy include each formal intervention for improving language and communication abilities. In the chronic stage after stroke, it is effective compared with no treatment, but its effect size is small. We present a new language training approach for the rehabilitation of patients with aphasia based on a brain-computer interface system. The approach exploits its capacity to provide feedback time-locked to a brain state. Thus, it implements the idea that reinforcing an appropriate language processing strategy may induce beneficial brain plasticity. In our approach, patients perform a simple auditory target word detection task whilst their EEG was recorded. The constant decoding of these signals by machine learning models generates an individual and immediate brain-state-dependent feedback. It indicates to patients how well they accomplish the task during a training session, even if they are unable to speak. Results obtained from a proof-of-concept study with 10 stroke patients with mild to severe chronic aphasia (age range: 38-76 years) are remarkable. First, we found that the high-intensity training (30 h, 4 days per week) was feasible, despite a high-word presentation speed and unfavourable stroke-induced EEG signal characteristics. Second, the training induced a sustained recovery of aphasia, which generalized to multiple language aspects beyond the trained task. Specifically, all tested language assessments (Aachen Aphasia Test, Snodgrass &amp; Vanderwart, Communicative Activity Log) showed significant medium to large improvements between pre- and post-training, with a standardized mean difference of 0.63 obtained for the Aachen Aphasia Test, and five patients categorized as non-aphasic at post-training assessment. Third, our data show that these language improvements were accompanied neither by significant changes in attention skills nor non-linguistic skills. Investigating possible modes of action of this brain-computer interface-based language training, neuroimaging data (EEG and resting-state functional MRI) indicates a training-induced faster word processing, a strengthened language network and a rebalancing between the language- and default mode networks.},
}
@article {pmid35174449,
year = {2022},
author = {Zhao, CG and Ju, F and Sun, W and Jiang, S and Xi, X and Wang, H and Sun, XL and Li, M and Xie, J and Zhang, K and Xu, GH and Zhang, SC and Mou, X and Yuan, H},
title = {Effects of Training with a Brain-Computer Interface-Controlled Robot on Rehabilitation Outcome in Patients with Subacute Stroke: A Randomized Controlled Trial.},
journal = {Neurology and therapy},
volume = {},
number = {},
pages = {},
pmid = {35174449},
issn = {2193-8253},
support = {81100932//National Natural Science Foundation of China/ ; 82072534//National Natural Science Foundation of China/ ; 91420301//National Natural Science Foundation of China/ ; 2021JM-232//Shaanxi Provincial Science and Technology Department/ ; 2020KW-050//Shaanxi Provincial Science and Technology Department/ ; 2018ZDCXL-GY-06-01//Shaanxi Provincial Science and Technology Department/ ; },
abstract = {INTRODUCTION: Stroke is always associated with a difficult functional recovery process. A brain-computer interface (BCI) is a technology which provides a direct connection between the human brain and external devices. The primary aim of this study was to determine whether training with a BCI-controlled robot can improve functions in patients with subacute stroke.<br><br>METHODS: Subacute stroke patients aged 32-68 years with a course of 2 weeks to 3 months were randomly assigned to the BCI group or to the sham group for a 4-week course. The primary outcome measures were Loewenstein Occupational Therapy Cognitive Assessment (LOCTA) and Fugl-Meyer Assessment for Lower Extremity (FMA-LE). Secondary outcome measures included Fugl-Meyer Assessment for Balance (FMA-B), Functional Ambulation Category (FAC), Modified Barthel Index (MBI), serum brain-derived neurotrophic factor (BDNF) levels and motor-evoked potential (MEP).<br><br>RESULTS: A total of 28 patients completed the study. Both groups showed a significant increase in mean LOCTA (sham: P < 0.001, Cohen's d = - 2.972; BCI: P < 0.001, Cohen's d = - 4.266) and FMA-LE (sham: P < 0.001, Cohen's d = - 3.178; BCI: P < 0.001, Cohen's d = - 3.063) scores. The LOCTA scores in the BCI group were 14.89% higher than in the sham group (P = 0.049, Cohen's d = - 0.580). There were no significant differences between the two groups in terms of FMA-B (P = 0.363, Cohen's d = - 0.252), FAC (P = 0.363), or MBI (P = 0.493, Cohen's d = - 0.188) scores. The serum levels of BDNF were significantly higher within the BCI group (P < 0.001, Cohen's d = - 1.167), and the MEP latency decreased by 3.75% and 4.71% in the sham and BCI groups, respectively.<br><br>CONCLUSION: Training with a BCI-controlled robot combined with traditional physiotherapy promotes cognitive function recovery, and enhances motor functions of the lower extremity in patients with subacute stroke. These patients also showed increased secretion of BDNF.<br><br>TRIAL REGISTRATION: Chinese clinical trial registry: ChiCTR-INR-17012874.},
}
@article {pmid35169837,
year = {2022},
author = {Nagata, K and Kunii, N and Shimada, S and Fujitani, S and Takasago, M and Saito, N},
title = {Spatiotemporal target selection for intracranial neural decoding of abstract and concrete semantics.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {},
number = {},
pages = {},
doi = {10.1093/cercor/bhac034},
pmid = {35169837},
issn = {1460-2199},
support = {19 K09452//Japan Society for the Promotion of Science/ ; },
abstract = {Decoding the inner representation of a word meaning from human cortical activity is a substantial challenge in the development of speech brain-machine interfaces (BMIs). The semantic aspect of speech is a novel target of speech decoding that may enable versatile communication platforms for individuals with impaired speech ability; however, there is a paucity of electrocorticography studies in this field. We decoded the semantic representation of a word from single-trial cortical activity during an imageability-based property identification task that required participants to discriminate between the abstract and concrete words. Using high gamma activity in the language-dominant hemisphere, a support vector machine classifier could discriminate the 2-word categories with significantly high accuracy (73.1 ± 7.5%). Activities in specific time components from two brain regions were identified as significant predictors of abstract and concrete dichotomy. Classification using these feature components revealed that comparable prediction accuracy could be obtained based on a spatiotemporally targeted decoding approach. Our study demonstrated that mental representations of abstract and concrete word processing could be decoded from cortical high gamma activities, and the coverage of implanted electrodes and time window of analysis could be successfully minimized. Our findings lay the foundation for the future development of semantic-based speech BMIs.},
}
@article {pmid35169437,
year = {2022},
author = {Park, A and Principe, DR},
title = {Blunt cardiac injury presenting as a left-sided coronary artery dissection.},
journal = {Journal of surgical case reports},
volume = {2022},
number = {2},
pages = {rjac008},
pmid = {35169437},
issn = {2042-8812},
support = {F30 CA236031/CA/NCI NIH HHS/United States ; },
abstract = {The presentation of blunt cardiac injuries (BCIs) following thoracic trauma is extremely varied, classically affecting the right-sided chambers of the heart. In extremely rare cases, BCIs can affect the coronary arteries. Diagnosing a traumatic coronary dissection can be challenging, as not only is presentation highly variable, but dissections are often masked by concomitant injuries. Here, we present the unusual case of a patient presenting to the emergency department following blunt thoracic trauma from an automobile accident. He demonstrated diffuse S and T wave segment elevations on electrocardiogram, and coronary angiography was significant for occlusion of the apical left anterior descending artery and stenosis of the second obtuse marginal artery. The patient was diagnosed with a BCI causing a left-sided coronary artery dissection. This serves as an important reminder that BCIs can manifest in any part of the cardiac anatomy, and should be considered in any patient with a history of thoracic trauma.},
}
@article {pmid35169105,
year = {2022},
author = {Zhang, H and Gomez, LJ and Guilleminot, J},
title = {Uncertainty quantification of TMS simulations considering MRI segmentation errors.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac5586},
pmid = {35169105},
issn = {1741-2552},
support = {R00 MH120046/MH/NIMH NIH HHS/United States ; },
mesh = {Brain/physiology ; Brain Mapping/methods ; *Magnetic Resonance Imaging/methods ; *Transcranial Magnetic Stimulation/methods ; Uncertainty ; },
abstract = {Objective.Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation method that is used to study brain function and conduct neuropsychiatric therapy. Computational methods that are commonly used for electric field (E-field) dosimetry of TMS are limited in accuracy and precision because of possible geometric errors introduced in the generation of head models by segmenting medical images into tissue types. This paper studies E-field prediction fidelity as a function of segmentation accuracy.Approach.The errors in the segmentation of medical images into tissue types are modeled as geometric uncertainty in the shape of the boundary between tissue types. For each tissue boundary realization, we then use an in-house boundary element method to perform a forward propagation analysis and quantify the impact of tissue boundary uncertainties on the induced cortical E-field.Main results.Our results indicate that predictions of E-field induced in the brain are negligibly sensitive to segmentation errors in scalp, skull and white matter (WM), compartments. In contrast, E-field predictions are highly sensitive to possible cerebrospinal fluid (CSF) segmentation errors. Specifically, the segmentation errors on the CSF and gray matter interface lead to higher E-field uncertainties in the gyral crowns, and the segmentation errors on CSF and WM interface lead to higher uncertainties in the sulci. Furthermore, the uncertainty of the average cortical E-fields over a region exhibits lower uncertainty relative to point-wise estimates.Significance.The accuracy of current cortical E-field simulations is limited by the accuracy of CSF segmentation accuracy. Other quantities of interest like the average of the E-field over a cortical region could provide a dose quantity that is robust to possible segmentation errors.},
}
@article {pmid35168083,
year = {2022},
author = {Khademi, Z and Ebrahimi, F and Kordy, HM},
title = {A transfer learning-based CNN and LSTM hybrid deep learning model to classify motor imagery EEG signals.},
journal = {Computers in biology and medicine},
volume = {143},
number = {},
pages = {105288},
doi = {10.1016/j.compbiomed.2022.105288},
pmid = {35168083},
issn = {1879-0534},
abstract = {In the Motor Imagery (MI)-based Brain Computer Interface (BCI), users' intention is converted into a control signal through processing a specific pattern in brain signals reflecting motor characteristics. There are such restrictions as the limited size of the existing datasets and low signal to noise ratio in the classification of MI Electroencephalogram (EEG) signals. Machine learning (ML) methods, particularly Deep Learning (DL), have overcome these limitations relatively. In this study, three hybrid models were proposed to classify the EEG signal in the MI-based BCI. The proposed hybrid models consist of the convolutional neural networks (CNN) and the Long-Short Term Memory (LSTM). In the first model, the CNN with different number of convolutional-pooling blocks (from shallow to deep CNN) was examined; a two-block CNN model not affected by the vanishing gradient descent and yet able to extract desirable features employed; the second and third models contained pre-trained CNNs conducing to the exploration of more complex features. The transfer learning strategy and data augmentation methods were applied to overcome the limited size of the datasets by transferring learning from one model to another. This was achieved by employing two powerful pre-trained convolutional neural networks namely ResNet-50 and Inception-v3. The continuous wavelet transform (CWT) was used to generate images for the CNN. The performance of the proposed models was evaluated on the BCI Competition IV dataset 2a. The mean accuracy vlaues of 86%, 90%, and 92%, and mean Kappa values of 81%, 86%, and 88% were obtained for the hybrid neural network with the customized CNN, the hybrid neural network with ResNet-50 and the hybrid neural network with Inception-v3, respectively. Despite the promising performance of the three proposed models, the hybrid neural network with Inception-v3 outperformed the two other models. The best obtained result in the present study improved the previous best result in the literature by 7% in terms of classification accuracy. From the findings, it can be concluded that transfer learning based on a pre-trained CNN in combination with LSTM is a novel method in MI-based BCI. The study also has implications for the discrimination of motor imagery tasks in each EEG recording channel and in different brain regions which can reduce computational time in future works by only selecting the most effective channels.},
}
@article {pmid35165308,
year = {2022},
author = {Nieto, N and Peterson, V and Rufiner, HL and Kamienkowski, JE and Spies, R},
title = {Thinking out loud, an open-access EEG-based BCI dataset for inner speech recognition.},
journal = {Scientific data},
volume = {9},
number = {1},
pages = {52},
pmid = {35165308},
issn = {2052-4463},
mesh = {Artificial Intelligence ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Speech Perception ; },
abstract = {Surface electroencephalography is a standard and noninvasive way to measure electrical brain activity. Recent advances in artificial intelligence led to significant improvements in the automatic detection of brain patterns, allowing increasingly faster, more reliable and accessible Brain-Computer Interfaces. Different paradigms have been used to enable the human-machine interaction and the last few years have broad a mark increase in the interest for interpreting and characterizing the "inner voice" phenomenon. This paradigm, called inner speech, raises the possibility of executing an order just by thinking about it, allowing a "natural" way of controlling external devices. Unfortunately, the lack of publicly available electroencephalography datasets, restricts the development of new techniques for inner speech recognition. A ten-participant dataset acquired under this and two others related paradigms, recorded with an acquisition system of 136 channels, is presented. The main purpose of this work is to provide the scientific community with an open-access multiclass electroencephalography database of inner speech commands that could be used for better understanding of the related brain mechanisms.},
}
@article {pmid35158119,
year = {2022},
author = {Liu, G and Tian, L and Zhou, W},
title = {Multiscale time-frequency method for multiclass Motor Imagery Brain Computer Interface.},
journal = {Computers in biology and medicine},
volume = {143},
number = {},
pages = {105299},
doi = {10.1016/j.compbiomed.2022.105299},
pmid = {35158119},
issn = {1879-0534},
abstract = {Motor Imagery Brain Computer Interface (MI-BCI) has become a promising technology in the field of neurorehabilitation. However, the performance and computational complexity of the current multiclass MI-BCI have not been fully optimized, and the intuitive interpretation of individual differences on motor imagery tasks is seldom investigated. In this paper, a well-designed multiscale time-frequency segmentation scheme is first applied to multichannel EEG recordings to obtain Time-Frequency Segments (TFSs). Then, the TFS selection based on a specific wrapper feature selection rule is utilized to determine optimum TFSs. Next, One-Versus-One (OvO)-divCSP implemented in divergence framework is used to extract discriminative features. Finally, One-Versus-Rest (OvR)-SVM is utilized to predict the class label based on selected multiclass MI features. Experimental results indicate our method yields a superior performance on two publicly available multiclass MI datasets with a mean accuracy of 80.00% and a mean kappa of 0.73. Meanwhile, the proposed TFS selection method can significantly alleviate the computational burden with little accuracy reduction, demonstrating the feasibility of real-time multiclass MI-BCI. Furthermore, the Motor Imagery Time-Frequency Reaction Map (MI-TFRM) is visualized, contributing to analyzing and interpreting the performance differences between different subjects.},
}
@article {pmid35158052,
year = {2022},
author = {Albani, S and Stolfo, D and Venkateshvaran, A and Chubuchny, V and Biondi, F and De Luca, A and Lo Giudice, F and Pasanisi, EM and Petersen, C and Airò, E and Bauleo, C and Ciardetti, M and Coceani, M and Formichi, B and Spiesshoefer, J and Savarese, G and Lund, LH and Emdin, M and Sinagra, G and Manouras, A and Giannoni, A and , },
title = {Echocardiographic Biventricular Coupling Index to Predict Precapillary Pulmonary Hypertension.},
journal = {Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.echo.2022.02.003},
pmid = {35158052},
issn = {1097-6795},
abstract = {BACKGROUND: Pulmonary hypertension (PH) is a frequent and detrimental condition. Right heart catheterization (RHC) is the gold standard to identify PH subtype (precapillary from postcapillary PH) and is key for treatment allocation. In this study, the novel echocardiographic biventricular coupling index (BCI), based on the ratio between right ventricular stroke work index and left ventricular E/E' ratio, was tested for the discrimination of PH subtype using RHC as the comparator.<br><br>METHODS: BCI was derived in 334 consecutive patients who underwent transthoracic echocardiography and RHC for all indications. BCI was then tested in a validation cohort of 1,349 patients.<br><br>RESULTS: The accuracy of BCI to identify precapillary PH was high in the derivation cohort (area under the curve, 0.82; 95% CI, 0.78-0.88; P < .001; optimal cut point, 1.9). BCI identified patients with precapillary PH with high accuracy also in the validation cohort (area under the curve, 0.87 [95% CI, 0.85-0.89; P < .001]; subgroup with PH: area under the curve, 0.91 [95% CI, 0.89-0.93; P < .001]; cut point, 1.9; sensitivity, 82%; specificity, 89%; positive predictive value, 77%; negative predictive value, 92%). BCI outperformed both the D'Alto score (Z = 3.56; difference between areas = 0.05; 95% CI, 0.02-0.07; P < .001) and the echocardiographic pulmonary-to-left atrial ratio index (Z = 2.88; difference between areas = 0.02; 95% CI, 0.01-0.04; P = .004).<br><br>CONCLUSIONS: BCI is a novel, noninvasive index based on routinely available echocardiographic parameters that identifies with high accuracy patients with precapillary PH. BCI may be of value in the screening workup of patients with PH.},
}
@article {pmid35153708,
year = {2021},
author = {Haruvi, A and Kopito, R and Brande-Eilat, N and Kalev, S and Kay, E and Furman, D},
title = {Measuring and Modeling the Effect of Audio on Human Focus in Everyday Environments Using Brain-Computer Interface Technology.},
journal = {Frontiers in computational neuroscience},
volume = {15},
number = {},
pages = {760561},
pmid = {35153708},
issn = {1662-5188},
abstract = {The goal of this study was to investigate the effect of audio listened to through headphones on subjectively reported human focus levels, and to identify through objective measures the properties that contribute most to increasing and decreasing focus in people within their regular, everyday environment. Participants (N = 62, 18-65 years) performed various tasks on a tablet computer while listening to either no audio (silence), popular audio playlists designed to increase focus (pre-recorded music arranged in a particular sequence of songs), or engineered soundscapes that were personalized to individual listeners (digital audio composed in real-time based on input parameters such as heart rate, time of day, location, etc.). Audio stimuli were delivered to participants through headphones while their brain signals were simultaneously recorded by a portable electroencephalography headband. Participants completed four 1-h long sessions at home during which different audio played continuously in the background. Using brain-computer interface technology for brain decoding and based on an individual's self-report of their focus, we obtained individual focus levels over time and used this data to analyze the effects of various properties of the sounds contained in the audio content. We found that while participants were working, personalized soundscapes increased their focus significantly above silence (p = 0.008), while music playlists did not have a significant effect. For the young adult demographic (18-36 years), all audio tested was significantly better than silence at producing focus (p = 0.001-0.009). Personalized soundscapes increased focus the most relative to silence, but playlists of pre-recorded songs also increased focus significantly during specific time intervals. Ultimately we found it is possible to accurately predict human focus levels a priori based on physical properties of audio content. We then applied this finding to compare between music genres and revealed that classical music, engineered soundscapes, and natural sounds were the best genres for increasing focus, while pop and hip-hop were the worst. These insights can enable human and artificial intelligence composers to produce increases or decreases in listener focus with high temporal (millisecond) precision. Future research will include real-time adaptation of audio for other functional objectives beyond affecting focus, such as affecting listener enjoyment, drowsiness, stress and memory.},
}
@article {pmid35151668,
year = {2022},
author = {Norizadeh Cherloo, M and Kashefi Amiri, H and Daliri, MR},
title = {Spatio-Spectral CCA (SS-CCA): A novel approach for frequency recognition in SSVEP-based BCI.},
journal = {Journal of neuroscience methods},
volume = {371},
number = {},
pages = {109499},
doi = {10.1016/j.jneumeth.2022.109499},
pmid = {35151668},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {BACKGROUND: Steady-state visually evoked potentials (SSVEP) are one of the most important paradigms in the BCI Domain. Among the best methods for detecting frequency in the SSVEP-based BCI is the Canonical Correlation Analysis (CCA), which calculates canonical correlation between two sets of multidimensional variables, the electroencephalogram (EEG) and reference signals. Despite its efficiency and widespread application, CCA algorithm has some limitations. One major limitation of CCA is to only consider the spatial domain information of the signal.<br><br>NEW METHOD: However, regarding frequency of signal as another critical feature of the signals, combining both spatial and frequency domain information can significantly improve the performance of frequency recognition. Although several previous studies about CCA algorithm, could improve its performance, they have not addressed CCA algorithm's limitation. To address this concern, in the current study, we presented Spatio-Spectral CCA (SS-CCA) algorithm, which is inspired from Common Spatio-Spectral Patterns (CSSP) algorithm. In the SS-CCA algorithm, we added a time delay to the EEG signal, in order to simultaneously optimize spatial and frequency information and obtain the canonical variables. Accordingly, for correlation coefficient's calculations, more information from EEG signal is utilized.<br><br>RESULTS: Finally, SS-CCA algorithm which is used as the base model of Filter Bank CCA (FBCCA), and Filter Bank SS-CCA algorithms, can help increase the frequency recognition performance. In order to evaluate the proposed method, 35-subject benchmark dataset were used. Proposed algorithm yielded mean accuracy 98.33 across all subjects.<br><br>Our classification accuracy and Information Transfer Rate (ITR) results showed that the performance of the above-mentioned method improves in comparison to the CCA.<br><br>CONCLUSIONS: In conclusion, using the proposed SS-CCA algorithm instead of the CCA, in all our experiments the CCA-based methods were improved.},
}
@article {pmid35151667,
year = {2022},
author = {Li, M and Zhang, P and Yang, G and Xu, G and Guo, M and Liao, W},
title = {A fisher linear discriminant analysis classifier fused with naïve Bayes for simultaneous detection in an asynchronous brain-computer interface.},
journal = {Journal of neuroscience methods},
volume = {371},
number = {},
pages = {109496},
doi = {10.1016/j.jneumeth.2022.109496},
pmid = {35151667},
issn = {1872-678X},
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; },
abstract = {BACKGROUND: An asynchronous event-related potential-based brain computer interface (ERP-BCI) permits the subjects to output intentions at their own pace, which provides a more free and practical communication pathways without the need for muscle activity. The core of constructing this type of system is to discriminate both the intentions and brain states.<br><br>NEW METHODS: This study proposes a fisher linear discriminant analysis classification algorithm fused with naïve Bayes (B-FLDA) for the ERP-BCI to simultaneous recognize the subjects' intentions, working and idle states. This method uses the spectral characteristics of visual-evoked potential and the time-domain characteristics of ERP to simultaneously detect brain states and target stimulus, and obtain the final discrimination result through probability fusion.<br><br>RESULTS: The accuracy and the information transfer rate increase to 98.61% and 62.80 bits/min under 10 repetitions and 1 repetition, respectively. The three parameters of receiver operator characteristic curve have achieved better performance.<br><br>Ten subjects participate in this study with the proposed algorithms and two other control methods. The accuracy and information transfer rate of this algorithm are better than the other methods.<br><br>CONCLUSIONS: It indicates that the naïve Bayes-FLDA algorithm is able to improve the performance of an asynchronous BCI system by detecting the intentions and states simultaneously.},
}
@article {pmid35151665,
year = {2022},
author = {Ma, P and Dong, C and Lin, R and Ma, S and Jia, T and Chen, X and Xiao, Z and Qi, Y},
title = {A classification algorithm of an SSVEP brain-Computer interface based on CCA fusion wavelet coefficients.},
journal = {Journal of neuroscience methods},
volume = {371},
number = {},
pages = {109502},
doi = {10.1016/j.jneumeth.2022.109502},
pmid = {35151665},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Support Vector Machine ; },
abstract = {BACKGROUND: In the study of brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs), how to improve the classification accuracies of BCIs has always been the focus of researchers. Canonical correlation analysis (CCA) is widely used in BCI systems of SSVEPs because of its rapidity and scalability. However, the classical CCA algorithm always encounters the difficulty of low accuracy in a short time.<br><br>NEW METHOD: For targetless stimuli, this paper proposes a fusion algorithm (CCA-CWT-SVM) that is combined with CCA, a continuous wavelet transform, and a support vector machine (SVM) to improve the low classification accuracies when a single feature extraction method is used.<br><br>RESULTS: This fusion algorithm achieves high accuracies and information transfer rates (ITRs) in the SSVEP paradigm with few targets.<br><br>Through the study of 400 groups of experimental data from 10 subjects, the results show that CCA-CWT-SVM has a classification accuracy of 91.76% within 2 s and an ITR of 48.92 bits/min, which are 10.88% and 13.18 bits/min higher than those of the standard CCA. Compared with a mainstream EEG decoding algorithm, filter bank canonical correlation analysis (FBCCA), the classification accuracy and ITR of the CCA-CWT-SVM algorithm also improved (4.45% and 5.69 bit/min, respectively). Using a dataset from Tsinghua University (THU), we also showed that the fusion algorithm is better than the classical algorithms. The CCA-CWT-SVM algorithm obtained an 89.1% accuracy and a 39.91 bit/min ITR in a time window of 2 s. The results were significantly improved compared with those of CCA and the FBCCA (CCA: 79.44% and 28.23 bits/min, FBCCA: 84.03% and 33.4 bits/min). Hence, this work provides an experimental basis for designing an SSVEP-based BCI system with a high task classification accuracy in some crucial biomedical applications.},
}
@article {pmid35150764,
year = {2022},
author = {Blanco-Diaz, CF and Antelis, JM and Ruiz-Olaya, AF},
title = {Comparative analysis of spectral and temporal combinations in CSP-based methods for decoding hand motor imagery tasks.},
journal = {Journal of neuroscience methods},
volume = {371},
number = {},
pages = {109495},
doi = {10.1016/j.jneumeth.2022.109495},
pmid = {35150764},
issn = {1872-678X},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: A widely used paradigm for brain-computer interfaces (BCI) is based on the detection of event-related (des)synchronization (ERD/S) in response to hand motor imagery (MI) tasks. The common spatial pattern (CSP) has been recognized as a powerful algorithm to design spatial filters for ERD/ERS detection. However, a limitation of CSP focus on identification only of discriminative spatial information but not the spectral one.<br><br>NEW METHOD: An open problem remains in literature related to extracting the most discriminative brain patterns in MI-based BCIs using an optimal time segment and spectral information that accounts for intersubject variability. In recent years, different variants of CSP-based methods have been proposed to address the problem of decoding motor imagery tasks under the intersubject variability of frequency bands related to ERD/ERS events, including Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatio-Spectral Patterns (FBCSSP).<br><br>We performed a comparative study of different combinations of time segments and filter banks for three methods (CSP, FBCSP, and FBCSSP) to decode hand (right and left) motor imagery tasks using two different EEG datasets (Gigascience and BCI IVa competition).<br><br>RESULTS: The best configuration corresponds to a filter bank with 3 filters (8-15 Hz, 15-22 Hz and 22-29 Hz) using a time window of 1.5 s after the trigger, which provide accuracies of approximately 74% and an estimated ITRs of approximately 7 bits/min.<br><br>CONCLUSION: Discriminative information in time and spectral domains could be obtained using a convenient filter bank and a time segment configuration, to enhance the classification rate and ITR for detection of hand motor imagery tasks with CSP-related methods, to be used in the implementation of a real-time BCI system.},
}
@article {pmid35148034,
year = {2022},
author = {Gates, EDH and Celaya, A and Suki, D and Schellingerhout, D and Fuentes, D},
title = {An efficient magnetic resonance image data quality screening dashboard.},
journal = {Journal of applied clinical medical physics},
volume = {23},
number = {4},
pages = {e13557},
pmid = {35148034},
issn = {1526-9914},
support = {R21 CA249373/CA/NCI NIH HHS/United States ; T15 LM007093/LM/NLM NIH HHS/United States ; 1R21CA249373/BC/NCI NIH HHS/United States ; },
mesh = {*Artificial Intelligence ; *Brain Neoplasms ; Data Accuracy ; Humans ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging/methods ; },
abstract = {PURPOSE: Complex data processing and curation for artificial intelligence applications rely on high-quality data sets for training and analysis. Manually reviewing images and their associated annotations is a very laborious task and existing quality control tools for data review are generally limited to raw images only. The purpose of this work was to develop an imaging informatics dashboard for the easy and fast review of processed magnetic resonance (MR) imaging data sets; we demonstrated its ability in a large-scale data review.<br><br>METHODS: We developed a custom R Shiny dashboard that displays key static snapshots of each imaging study and its annotations. A graphical interface allows the structured entry of review data and download of tabulated review results. We evaluated the dashboard using two large data sets: 1380 processed MR imaging studies from our institution and 285 studies from the 2018 MICCAI Brain Tumor Segmentation Challenge (BraTS).<br><br>RESULTS: Studies were reviewed at an average rate of 100/h using the dashboard, 10 times faster than using existing data viewers. For data from our institution, 1181 of the 1380 (86%) studies were of acceptable quality. The most commonly identified failure modes were tumor segmentation (9.6% of cases) and image registration (4.6% of cases). Tumor segmentation without visible errors on the dashboard had much better agreement with reference tumor volume measurements (root-mean-square error 12.2 cm3) than did segmentations with minor errors (20.5 cm3) or failed segmentations (27.4 cm3). In the BraTS data, 242 of 285 (85%) studies were acceptable quality after processing. Among the 43 cases that failed review, 14 had unacceptable raw image quality.<br><br>CONCLUSION: Our dashboard provides a fast, effective tool for reviewing complex processed MR imaging data sets. It is freely available for download at https://github.com/EGates1/MRDQED.},
}
@article {pmid35147515,
year = {2022},
author = {Basti, A and Chella, F and Guidotti, R and Ermolova, M and D'Andrea, A and Stenroos, M and Romani, GL and Pizzella, V and Marzetti, L},
title = {Looking through the windows: a study about the dependency of phase-coupling estimates on the data length.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac542f},
pmid = {35147515},
issn = {1741-2552},
mesh = {Brain/physiology ; *Brain Mapping/methods ; Electroencephalography/methods ; *Magnetoencephalography/methods ; Reproducibility of Results ; },
abstract = {Objective. Being able to characterize functional connectivity (FC) state dynamics in a real-time setting, such as in brain-computer interface, neurofeedback or closed-loop neurostimulation frameworks, requires the rapid detection of the statistical dependencies that quantify FC in short windows of data. The aim of this study is to characterize, through extensive realistic simulations, the reliability of FC estimation as a function of the data length. In particular, we focused on FC as measured by phase-coupling (PC) of neuronal oscillations, one of the most functionally relevant neural coupling modes.Approach. We generated synthetic data corresponding to different scenarios by varying the data length, the signal-to-noise ratio (SNR), the phase difference value, the spectral analysis approach (Hilbert or Fourier) and the fractional bandwidth. We compared seven PC metrics, i.e. imaginary part of phase locking value (iPLV), PLV of orthogonalized signals, phase lag index (PLI), debiased weighted PLI, imaginary part of coherency, coherence of orthogonalized signals and lagged coherence.Main results. Our findings show that, for a SNR of at least 10 dB, a data window that contains 5-8 cycles of the oscillation of interest (e.g. a 500-800 ms window at 10 Hz) is generally required to achieve reliable PC estimates. In general, Hilbert-based approaches were associated with higher performance than Fourier-based approaches. Furthermore, the results suggest that, when the analysis is performed in a narrow frequency range, a larger window is required.Significance. The achieved results pave the way to the introduction of best-practice guidelines to be followed when a real-time frequency-specific PC assessment is at target.},
}
@article {pmid35146564,
year = {2022},
author = {Carino-Escobar, RI and Rodriguez-Barragan, MA and Carrillo-Mora, P and Cantillo-Negrete, J},
title = {Brain-computer interface as complementary therapy for hemiparesis in an astrocytoma patient.},
journal = {Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology},
volume = {43},
number = {4},
pages = {2879-2881},
pmid = {35146564},
issn = {1590-3478},
support = {SALUD-2018-02-B-S-45803//Consejo Nacional de Ciencia y Tecnología/ ; },
mesh = {*Astrocytoma/complications/diagnostic imaging/therapy ; *Brain Neoplasms/complications/diagnostic imaging/therapy ; *Brain-Computer Interfaces ; *Complementary Therapies ; Humans ; Paresis/etiology/therapy ; },
}
@article {pmid35145386,
year = {2021},
author = {Gonzalez-Navarro, P and Celik, B and Moghadamfalahi, M and Akcakaya, M and Fried-Oken, M and Erdoğmuş, D},
title = {Feedback Related Potentials for EEG-Based Typing Systems.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {788258},
pmid = {35145386},
issn = {1662-5161},
abstract = {Error related potentials (ErrP), which are elicited in the EEG in response to a perceived error, have been used for error correction and adaption in the event related potential (ERP)-based brain computer interfaces designed for typing. In these typing interfaces, ERP evidence is collected in response to a sequence of stimuli presented usually in the visual form and the intended user stimulus is probabilistically inferred (stimulus with highest probability) and presented to the user as the decision. If the inferred stimulus is incorrect, ErrP is expected to be elicited in the EEG. Early approaches to use ErrP in the design of typing interfaces attempt to make hard decisions on the perceived error such that the perceived error is corrected and either the sequence of stimuli are repeated to obtain further ERP evidence, or without further repetition the stimulus with the second highest probability is presented to the user as the decision of the system. Moreover, none of the existing approaches use a language model to increase the performance of typing. In this work, unlike the existing approaches, we study the potential benefits of fusing feedback related potentials (FRP), a form of ErrP, with ERP and context information (language model, LM) in a Bayesian fashion to detect the user intent. We present experimental results based on data from 12 healthy participants using RSVP Keyboard™ to complete a copy-phrase-task. Three paradigms are compared: [P1] uses only ERP/LM Bayesian fusion; [P2] each RSVP sequence is appended with the top candidate in the alphabet according to posterior after ERP evidence fusion; corresponding FRP is then incorporated; and [P3] the top candidate is shown as a prospect to generate FRP evidence only if its posterior exceeds a threshold. Analyses indicate that ERP/LM/FRP evidence fusion during decision making yields significant speed-accuracy benefits for the user.},
}
@article {pmid35144966,
year = {2022},
author = {Bartlett, JMS and Sgroi, DC and Treuner, K and Zhang, Y and Piper, T and Salunga, RC and Ahmed, I and Doos, L and Thornber, S and Taylor, KJ and Brachtel, EF and Pirrie, SJ and Schnabel, CA and Rea, D},
title = {Breast Cancer Index is a predictive biomarker of treatment benefit and outcome from extended tamoxifen therapy: final analysis of the Trans-aTTom study.},
journal = {Clinical cancer research : an official journal of the American Association for Cancer Research},
volume = {},
number = {},
pages = {},
doi = {10.1158/1078-0432.CCR-21-3385},
pmid = {35144966},
issn = {1557-3265},
support = {12125/CRUK_/Cancer Research UK/United Kingdom ; },
abstract = {PURPOSE: The Breast Cancer Index (BCI) HOXB13/IL17BR (H/I) ratio predicts benefit from extended endocrine therapy in hormone receptor-positive (HR+) early-stage breast cancer. Here we report the final analysis of the Trans-aTTom study examining BCI (H/I)'s predictive performance.<br><br>EXPERIMENTAL DESIGN: BCI results were available for 2445 aTTom trial patients. The primary endpoint of recurrence-free interval (RFI) and secondary endpoints of disease-free interval (DFI) and disease-free survival (DFS) were examined using Cox proportional hazards regression and log-rank test.<br><br>RESULTS: Final analysis of the overall study population (N=2445) did not show a significant improvement in RFI with extended tamoxifen (HR=0.90; 95% CI: 0.69-1.16; P=0.401). Both the overall study population and N0 group were underpowered due to the low event rate in the N0 group. In a pre-planned analysis of the N+ subset (N=789), BCI (H/I)-High patients derived significant benefit from extended tamoxifen (9.7% absolute benefit: HR=0.33; 95% CI 0.14-0.75; P=0.016), whereas BCI (H/I)-Low patients did not (-1.2% absolute benefit; HR=1.11; 95% CI 0.76-1.64; P=0.581). A significant treatment-to-biomarker interaction was demonstrated based on RFI, DFI and DFS (P=0.037, 0.040, and 0.025, respectively). BCI (H/I)-High patients remained predictive of benefit from extended tamoxifen in the N+/HER2- subgroup (9.4% absolute benefit: HR=0.35; 95% CI 0.15-0.81; P=0.047). A three-way interaction evaluating BCI (H/I), treatment, and HER2 status was not statistically significant (P=0.849).<br><br>CONCLUSIONS: Novel findings demonstrate that BCI (H/I) significantly predicts benefit from extended tamoxifen in HR+ N+ patients with HER2- disease. Moreover, BCI (H/I) demonstrates significant treatment to biomarker interaction across survival outcomes.},
}
@article {pmid35143920,
year = {2022},
author = {Rosipal, R and Rošťáková, Z and Trejo, LJ},
title = {Tensor decomposition of human narrowband oscillatory brain activity in frequency, space and time.},
journal = {Biological psychology},
volume = {169},
number = {},
pages = {108287},
doi = {10.1016/j.biopsycho.2022.108287},
pmid = {35143920},
issn = {1873-6246},
mesh = {Algorithms ; *Brain/physiology ; *Electroencephalography/methods ; Humans ; },
abstract = {Many brain processes in health and disease are associated with modulation of narrowband brain oscillations (NBOs) in the scalp-recorded EEG, which exhibit specific frequency spectra and scalp topography. Isolating and tracking NBOs over time using algorithms is useful in domains such as brain-computer interfaces or when measuring the EEG effects of experimental manipulations. Previously, we successfully applied modified tensor methods for identifying and tracking NBO activity over time or conditions. We introduced frequency and spatial constraints that greatly improved their physiological plausibility. In this paper we rigorously demonstrate the power and precision of tensor methods to separate, isolate and track NBOs using sources simulated with an anatomical forward model. This allows us to control the attributes of NBOs and validate tensor solutions. We find that tensor methods can accurately identify, separate and track NBOs over time, using realistic sources either alone or in combination, and compare favorably to well-known spatio-spectral decomposition methods for NBO estimation.},
}
@article {pmid35143159,
year = {2022},
author = {Krishnaprasad, A and Dev, D and Han, SS and Shen, Y and Chung, HS and Bae, TS and Yoo, C and Jung, Y and Lanza, M and Roy, T},
title = {MoS2 Synapses with Ultra-low Variability and Their Implementation in Boolean Logic.},
journal = {ACS nano},
volume = {16},
number = {2},
pages = {2866-2876},
doi = {10.1021/acsnano.1c09904},
pmid = {35143159},
issn = {1936-086X},
mesh = {Brain ; *Molybdenum ; Neurons/physiology ; *Synapses/physiology ; },
abstract = {Brain-inspired computing enabled by memristors has gained prominence over the years due to the nanoscale footprint and reduced complexity for implementing synapses and neurons. The demonstration of complex neuromorphic circuits using conventional materials systems has been limited by high cycle-to-cycle and device-to-device variability. Two-dimensional (2D) materials have been used to realize transparent, flexible, ultra-thin memristive synapses for neuromorphic computing, but with limited knowledge on the statistical variation of devices. In this work, we demonstrate ultra-low-variability synapses using chemical vapor deposited 2D MoS2 as the switching medium with Ti/Au electrodes. These devices, fabricated using a transfer-free process, exhibit ultra-low variability in SET voltage, RESET power distribution, and synaptic weight update characteristics. This ultra-low variability is enabled by the interface rendered by a Ti/Au top contact on Si-rich MoS2 layers of mixed orientation, corroborated by transmission electron microscopy (TEM), electron energy loss spectroscopy (EELS), and X-ray photoelectron spectroscopy (XPS). TEM images further confirm the stability of the device stack even after subjecting the device to 100 SET-RESET cycles. Additionally, we implement logic gates by monolithic integration of MoS2 synapses with MoS2 leaky integrate-and-fire neurons to show the viability of these devices for non-von Neumann computing.},
}
@article {pmid35140763,
year = {2022},
author = {Fu, Y and Li, Z and Gong, A and Qian, Q and Su, L and Zhao, L},
title = {Identification of Visual Imagery by Electroencephalography Based on Empirical Mode Decomposition and an Autoregressive Model.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {1038901},
pmid = {35140763},
issn = {1687-5273},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagination ; Support Vector Machine ; },
abstract = {The traditional imagery task for brain-computer interfaces (BCIs) consists of motor imagery (MI) in which subjects are instructed to imagine moving certain parts of their body. This kind of imagery task is difficult for subjects. In this study, we used a less studied yet more easily performed type of mental imagery-visual imagery (VI)-in which subjects are instructed to visualize a picture in their brain to implement a BCI. In this study, 18 subjects were recruited and instructed to observe one of two visual-cued pictures (one was static, while the other was moving) and then imagine the cued picture in each trial. Simultaneously, electroencephalography (EEG) signals were collected. Hilbert-Huang Transform (HHT), autoregressive (AR) models, and a combination of empirical mode decomposition (EMD) and AR were used to extract features, respectively. A support vector machine (SVM) was used to classify the two kinds of VI tasks. The average, highest, and lowest classification accuracies of HHT were 68.14 ± 3.06%, 78.33%, and 53.3%, respectively. The values of the AR model were 56.29 ± 2.73%, 71.67%, and 30%, respectively. The values obtained by the combination of the EMD and the AR model were 78.40 ± 2.07%, 87%, and 48.33%, respectively. The results indicate that multiple VI tasks were separable based on EEG and that the combination of EMD and an AR model used in VI feature extraction was better than an HHT or AR model alone. Our work may provide ideas for the construction of a new online VI-BCI.},
}
@article {pmid35140593,
year = {2021},
author = {Meng, J and Wu, Z and Li, S and Zhu, X},
title = {Effects of Gaze Fixation on the Performance of a Motor Imagery-Based Brain-Computer Interface.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {773603},
pmid = {35140593},
issn = {1662-5161},
abstract = {Motor imagery-based brain-computer interfaces (BCIs) have been studied without controlling subjects' gaze fixation position previously. The effect of gaze fixation and covert attention on the behavioral performance of BCI is still unknown. This study designed a gaze fixation controlled experiment. Subjects were required to conduct a secondary task of gaze fixation when performing the primary task of motor imagination. Subjects' performance was analyzed according to the relationship between motor imagery target and the gaze fixation position, resulting in three BCI control conditions, i.e., congruent, incongruent, and center cross trials. A group of fourteen subjects was recruited. The average group performances of three different conditions did not show statistically significant differences in terms of BCI control accuracy, feedback duration, and trajectory length. Further analysis of gaze shift response time revealed a significantly shorter response time for congruent trials compared to incongruent trials. Meanwhile, the parietal occipital cortex also showed active neural activities for congruent and incongruent trials, and this was revealed by a contrast analysis of R-square values and lateralization index. However, the lateralization index computed from the parietal and occipital areas was not correlated with the BCI behavioral performance. Subjects' BCI behavioral performance was not affected by the position of gaze fixation and covert attention. This indicated that motor imagery-based BCI could be used freely in robotic arm control without sacrificing performance.},
}
@article {pmid35139061,
year = {2022},
author = {Du, Q and Luo, J and Chu, C and Wang, Y and Cheng, Q and Guo, S},
title = {The brain state of motor imagery is reflected in the causal information of functional near-infrared spectroscopy.},
journal = {Neuroreport},
volume = {33},
number = {3},
pages = {137-144},
doi = {10.1097/WNR.0000000000001765},
pmid = {35139061},
issn = {1473-558X},
mesh = {Brain/diagnostic imaging ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination/physiology ; Quality of Life ; *Spectroscopy, Near-Infrared/methods ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) is a promising neurorehabilitation strategy for ameliorating post-stroke function disorders. Physiological changes in the brain, such as functional near-infrared spectroscopy (fNIRS) dedicated to exploring cerebral circulatory responses during neurological rehabilitation tasks, are essential for gaining insights into neurorehabilitation mechanisms. However, the relationship between the neurovascular responses in different brain regions under rehabilitation tasks remains unknown.<br><br>OBJECTIVE: The present study explores the fNIRS interactions between brain regions under different motor imagery (MI) tasks, emphasizing functional characteristics of brain network patterns and BCI motor task classification.<br><br>METHODS: Granger causality analysis (GCA) is carried out for oxyhemoglobin data from 29 study participants in left- and right-hand MI tasks.<br><br>RESULTS: According to research findings, homozygous and heterozygous states in the two brain connectivity modes reveal one and nine channel pairs, respectively, with significantly different (P < 0.05) GC values under the left- and right-hand MI tasks in the population. With reference to the total 10 channel pairs of causality differences between the two brain working states, a support vector machine is used to classify the two tasks with an overall accuracy of 83% for five-fold cross-validation.<br><br>CONCLUSION: As demonstrated in the present study, fNIRS offers causality patterns in different brain states of MIBCI motor tasks. The research findings show that fNIRS causality can be used to assess different states of the brain, providing theoretical support for its application to neurorehabilitation assessment protocols to ultimately improve patients' quality of life.Video Abstract: http://links.lww.com/WNR/A653.},
}
@article {pmid35134085,
year = {2022},
author = {Xiong, W and Wei, Q},
title = {Reducing calibration time in motor imagery-based BCIs by data alignment and empirical mode decomposition.},
journal = {PloS one},
volume = {17},
number = {2},
pages = {e0263641},
pmid = {35134085},
issn = {1932-6203},
mesh = {Algorithms ; Brain-Computer Interfaces/psychology/*trends ; Calibration ; Discriminant Analysis ; Electroencephalography/methods ; Humans ; Image Processing, Computer-Assisted/*methods ; Logistic Models ; Models, Theoretical ; Signal Processing, Computer-Assisted/instrumentation ; Visual Perception/physiology ; },
abstract = {One of the major reasons that limit the practical applications of a brain-computer interface (BCI) is its long calibration time. In this paper, we propose a novel approach to reducing the calibration time of motor imagery (MI)-based BCIs without sacrificing classification accuracy. The approach aims to augment the training set size of a new subject by generating artificial electroencephalogram (EEG) data from a few training trials initially available. The artificial EEG data are obtained by first performing empirical mode decomposition (EMD) and then mixing resulting intrinsic mode functions (IMFs). The original training trials are aligned to common reference point with Euclidean alignment (EA) method prior to EMD and pooled together with artificial trials as the expended training set, which is input into a linear discriminant analysis (LDA) classifier or a logistic regression (LR) classifier. The performance of the proposed algorithm is evaluated on two motor imagery (MI) data sets and compared with that of the algorithm trained with only real EEG data (Baseline) and the algorithm trained with expanded EEG data by EMD without data alignment. The experimental results showed that the proposed algorithm can significantly reduce the amount of training data needed to achieve a given performance level and thus is expected to facilitate the real-world applications of MI-based BCIs.},
}
@article {pmid35133967,
year = {2022},
author = {Chen, C and Ma, Z and Liu, Z and Zhou, L and Wang, G and Li, Y and Zhao, J},
title = {An Energy-Efficient Wearable Functional Near-infrared Spectroscopy System Employing Dual-level Adaptive Sampling Technique.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2022.3149766},
pmid = {35133967},
issn = {1940-9990},
abstract = {Functional near-infrared spectroscopy (fNIRS) is a powerful medical imaging tool in brain science and psychology, it can also be employed in brain-computer interface (BCI) due to its noninvasive and artifact-less-sensitive characteristics. Conventional ways to detect large-area brain activity using near-infrared (NIR) technology is based on Time-division or Frequency-division modulation technique, which traverses all physical sensory channels in a specific period. To achieve higher imaging resolution or brain-tasks classification accuracy, the NIRS system require higher density and more channels, which conflict with the limited battery capacity. Inspired by the functional atlas of the human brain, this paper proposes a spatial adaptive sampling (SAS) method. It can change the active channel pattern of the fNIRS system to match with the real-time brain activity, to increase the energy efficiency without significant reduction on the brain imaging quality or the accuracy of brain activity classification. Therefore, the number of the averaging enabled channels will be dramatically reduced in practice. To verify the proposed SAS technique, a wearable and flexible NIRS system has been implemented, in which each channel of light-emitting diode drive circuits and photodiode detection circuits can be power gated independently. Brain task experiments have been conducted to validate the proposed method, the power consumption of the LED drive module is reduced by 46.58% compared to that without SAS technology while maintaining an average brain imaging peak signal to noise ratio of 35dB. The brain-task classification accuracy is 80.47%, which has a 2.67% reduction compared to that without the SAS technique.},
}
@article {pmid35133966,
year = {2022},
author = {Ni, Z and Xu, J and Wu, Y and Li, M and Xu, G and Xu, B},
title = {Improving Cross-State and Cross-Subject Visual ERP-Based BCI With Temporal Modeling and Adversarial Training.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {369-379},
doi = {10.1109/TNSRE.2022.3150007},
pmid = {35133966},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials/physiology ; Humans ; },
abstract = {Brain-computer interface (BCI) is a useful device for people without relying on peripheral nerves and muscles. However, the performance of the event-related potential (ERP)-based BCI declines when applying it to real environments, especially in cross-state and cross-subject conditions. Here we employ temporal modeling and adversarial training to improve the visual ERP-based BCI under different mental workload states and to alleviate the problems above. The rationality of our method is that the ERP-based BCI is based on electroencephalography (EEG) signals recorded from the scalp's surface, continuously changing with time and somewhat stochastic. In this paper, we propose a hierarchical recurrent network to encode all ERP signals in each repetition at the same time and model them with a temporal manner to predict which visual event elicited an ERP. The hierarchical architecture is a simple yet effective method for organizing recurrent layers in a deep structure to model long sequence signals. Taking a cue from recent advances in adversarial training, we further applied dynamic adversarial perturbations to create adversarial examples to enhance the model performance. We conduct our experiments on one published visual ERP-based BCI task with 15 subjects and 3 different auditory workload states. The results indicate that our hierarchical method can effectively model the long sequence EEG raw data, outperform the baselines on most conditions, including cross-state and cross-subject conditions. Finally, we show how deep learning-based methods with limited EEG data can improve ERP-based BCI with adversarial training. Our code is available at https://github.com/aispeech-lab/VisBCI.},
}
@article {pmid35133296,
year = {2022},
author = {Zhang, H and Gomez, L and Guilleminot, J},
title = {Uncertainty quantification of TMS simulations considering MRI segmentation errors.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ac52d1},
pmid = {35133296},
issn = {1741-2552},
abstract = {OBJECTIVE: Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation method that is used to study brain function and conduct neuropsychiatric therapy. Computational methods that are commonly used for electric field (E-field) dosimetry of TMS are limited in accuracy and precision because of possible geometric errors introduced in the generation of head models by segmenting medical images into tissue types. This paper studies E-field prediction fidelity as a function of segmentation accuracy.<br><br>APPROACH: The errors in the segmentation of medical images into tissue types are modeled as geometric uncertainty in the shape of the boundary between tissue types. For each tissue boundary realization, we then use an in-house boundary element method to perform a forward propagation analysis and quantify the impact of tissue boundary uncertainties on the induced cortical E-field.<br><br>MAIN RESULTS: Our results indicate that predictions of E-field induced in the brain are negligibly sensitive to segmentation errors in scalp, skull and white matter, compartments. In contrast, E-field predictions are highly sensitive to possible CSF segmentation errors. Specifically, the segmentation errors on the CSF and gray matter interface lead to higher E-field uncertainties in the gyral crowns, and the segmentation errors on CSF and white matter interface lead to higher uncertainties in the sulci. Furthermore, the uncertainty of the average cortical E-fields over a region exhibits lower uncertainty relative to point-wise estimates.<br><br>SIGNIFICANCE: The accuracy of current cortical E-field simulations is limited by the accuracy of CSF segmentation accuracy. Other quantities of interest like the average of the E-field over a cortical region could provide a dose quantity that is robust to possible segmentation errors.},
}
@article {pmid35133292,
year = {2022},
author = {Basti, A and Chella, F and Guidotti, R and Ermolova, M and D'Andrea, A and Stenroos, M and Romani, GL and Pizzella, V and Marzetti, L},
title = {Looking through the windows: a study about the dependency of phase-coupling estimates on the data length.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ac52d3},
pmid = {35133292},
issn = {1741-2552},
abstract = {OBJECTIVE: Being able to characterize functional connectivity (FC) state dynamics in a real-time setting, such as in brain-computer interface, neurofeedback or closed-loop neurostimulation frameworks, requires the rapid detection of the statistical dependencies that quantify FC in short windows of data. The aim of this study is to characterize, through extensive realistic simulations, the reliability of FC estimation as a function of the data length. In particular, we focused on FC as measured by phase-coupling (PC) of neuronal oscillations, one of the most functionally relevant neural coupling modes.<br><br>APPROACH: We generated synthetic data corresponding to different scenarios by varying the data length, the signal-to-noise ratio, the phase difference value, the spectral analysis approach (Hilbert or Fourier) and the fractional bandwidth. We compared seven PC metrics, i.e. imaginary part of phase locking value (PLV), PLV of orthogonalized signals, phase lag index (PLI), debiased weighted PLI, imaginary part of coherency, coherence of orthogonalized signals and lagged coherence.<br><br>MAIN RESULTS: Our findings show that, for a signal-to-noise-ratio of at least 10 dB, a data window that contains 5 to 8 cycles of the oscillation of interest (e.g. a 500-800ms window at 10Hz) is generally required to achieve reliable PC estimates. In general, Hilbert-based approaches were associated with higher performance than Fourier-based approaches. Furthermore, the results suggest that, when the analysis is performed in a narrow frequency range, a larger window is required.<br><br>SIGNIFICANCE: The achieved results pave the way to the introduction of best-practice guidelines to be followed when a real-time frequency-specific PC assessment is at target.},
}
@article {pmid35130536,
year = {2022},
author = {Zhang, Z and Savolainen, OW and Constandinou, TG},
title = {Algorithm and hardware considerations for real-time neural signal on-implant processing.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac5268},
pmid = {35130536},
issn = {1741-2552},
support = {/MRC_/Medical Research Council/United Kingdom ; },
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Computers ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {Objective.Various on-workstation neural-spike-based brain machine interface (BMI) systems have reached the point of in-human trials, but on-node and on-implant BMI systems are still under exploration. Such systems are constrained by the area and battery. Researchers should consider the algorithm complexity, available resources, power budgets, CMOS technologies, and the choice of platforms when designing BMI systems. However, the effect of these factors is currently still unclear.Approaches.Here we have proposed a novel real-time 128 channel spike detection algorithm and optimised it on microcontroller (MCU) and field programmable gate array (FPGA) platforms towards consuming minimal power and memory/resources. It is presented as a use case to explore the different considerations in system design.Main results.The proposed spike detection algorithm achieved over 97% sensitivity and a smaller than 3% false detection rate. The MCU implementation occupies less than 3 KB RAM and consumes 31.5 µW ch-1. The FPGA platform only occupies 299 logic cells and 3 KB RAM for 128 channels and consumes 0.04 µW ch-1.Significance.On the spike detection algorithm front, we have eliminated the processing bottleneck by reducing the dynamic power consumption to lower than the hardware static power, without sacrificing detection performance. More importantly, we have explored the considerations in algorithm and hardware design with respect to scalability, portability, and costs. These findings can facilitate and guide the future development of real-time on-implant neural signal processing platforms.},
}
@article {pmid35130163,
year = {2022},
author = {Kwak, Y and Song, WJ and Kim, SE},
title = {FGANet: fNIRS-Guided Attention Network for Hybrid EEG-fNIRS Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {329-339},
doi = {10.1109/TNSRE.2022.3149899},
pmid = {35130163},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; },
abstract = {Non-invasive brain-computer interfaces (BCIs) have been widely used for neural decoding, linking neural signals to control devices. Hybrid BCI systems using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention for overcoming the limitations of EEG- and fNIRS-standalone BCI systems. However, most hybrid EEG-fNIRS BCI studies have focused on late fusion because of discrepancies in their temporal resolutions and recording locations. Despite the enhanced performance of hybrid BCIs, late fusion methods have difficulty in extracting correlated features in both EEG and fNIRS signals. Therefore, in this study, we proposed a deep learning-based early fusion structure, which combines two signals before the fully-connected layer, called the fNIRS-guided attention network (FGANet). First, 1D EEG and fNIRS signals were converted into 3D EEG and fNIRS tensors to spatially align EEG and fNIRS signals at the same time point. The proposed fNIRS-guided attention layer extracted a joint representation of EEG and fNIRS tensors based on neurovascular coupling, in which the spatially important regions were identified from fNIRS signals, and detailed neural patterns were extracted from EEG signals. Finally, the final prediction was obtained by weighting the sum of the prediction scores of the EEG and fNIRS-guided attention features to alleviate performance degradation owing to delayed fNIRS response. In the experimental results, the FGANet significantly outperformed the EEG-standalone network. Furthermore, the FGANet has 4.0% and 2.7% higher accuracy than the state-of-the-art algorithms in mental arithmetic and motor imagery tasks, respectively.},
}
@article {pmid35130161,
year = {2022},
author = {Liu, G and Wang, J},
title = {EEGG: An Analytic Brain-Computer Interface Algorithm.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {643-655},
doi = {10.1109/TNSRE.2022.3149654},
pmid = {35130161},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Hand ; Humans ; Imagination/physiology ; },
abstract = {OBJECTIVE: Modeling the brain as a white box is vital for investigating the brain. However, the physical properties of the human brain are unclear. Therefore, BCI algorithms using EEG signals are generally a data-driven approach and generate a black- or gray-box model. This paper presents the first EEG-based BCI algorithm (EEG-BCI using Gang neurons, EEGG) decomposing the brain into some simple components with physical meaning and integrating recognition and analysis of brain activity.<br><br>APPROACH: Independent and interactive components of neurons or brain regions can fully describe the brain. This paper constructed a relation frame based on the independent and interactive compositions for intention recognition and analysis using a novel dendrite module of Gang neurons. A total of 4,906 EEG data of left- and right-hand motor imagery (MI) from 26 subjects were obtained from GigaDB. Firstly, this paper explored EEGG's classification performance by cross-subject accuracy. Secondly, this paper transformed the trained EEGG model into a relation spectrum expressing independent and interactive components of brain regions. Then, the relation spectrum was verified using the known ERD/ERS phenomenon. Finally, this paper explored the previously unreachable further BCI-based analysis of the brain.<br><br>MAIN RESULTS: (1) EEGG was more robust than typical "CSP+" algorithms for the low-quality data. (2) The relation spectrum showed the known ERD/ERS phenomenon. (3) Interestingly, EEGG showed that interactive components between brain regions suppressed ERD/ERS effects on classification. This means that generating fine hand intention needs more centralized activation in the brain.<br><br>SIGNIFICANCE: EEGG decomposed the biological EEG-intention system of this paper into the relation spectrum inheriting the Taylor series (in analogy with the data-driven but human-readable Fourier transform and frequency spectrum), which offers a novel frame for analysis of the brain.},
}
@article {pmid35126800,
year = {2021},
author = {Paek, AY and Brantley, JA and Evans, BJ and Contreras-Vidal, JL},
title = {Concerns in the Blurred Divisions between Medical and Consumer Neurotechnology.},
journal = {IEEE systems journal},
volume = {15},
number = {2},
pages = {3069-3080},
pmid = {35126800},
issn = {1932-8184},
support = {F99 NS105210/NS/NINDS NIH HHS/United States ; K00 NS105210/NS/NINDS NIH HHS/United States ; },
abstract = {Neurotechnology has traditionally been central to the diagnosis and treatment of neurological disorders. While these devices have initially been utilized in clinical and research settings, recent advancements in neurotechnology have yielded devices that are more portable, user-friendly, and less expensive. These improvements allow laypeople to monitor their brain waves and interface their brains with external devices. Such improvements have led to the rise of wearable neurotechnology that is marketed to the consumer. While many of the consumer devices are marketed for innocuous applications, such as use in video games, there is potential for them to be repurposed for medical use. How do we manage neurotechnologies that skirt the line between medical and consumer applications and what can be done to ensure consumer safety? Here, we characterize neurotechnology based on medical and consumer applications and summarize currently marketed uses of consumer-grade wearable headsets. We lay out concerns that may arise due to the similar claims associated with both medical and consumer devices, the possibility of consumer devices being repurposed for medical uses, and the potential for medical uses of neurotechnology to influence commercial markets related to employment and self-enhancement.},
}
@article {pmid35126771,
year = {2022},
author = {Ketu, S and Mishra, PK},
title = {Hybrid classification model for eye state detection using electroencephalogram signals.},
journal = {Cognitive neurodynamics},
volume = {16},
number = {1},
pages = {73-90},
pmid = {35126771},
issn = {1871-4080},
abstract = {The electroencephalography (EEG) signal is an essential source of Brain-Computer Interface (BCI) technology implementation. The BCI is nothing but a non-muscle communication medium among the external devices and the brain. The basic concept of BCI is to enable the interaction among the neurological ill patients to others with the help of brain signals. EEG signal classification is an essential requirement for various applications such as motor imagery classification, drug effects diagnosis, emotion classification, seizure prediction/detection, eye state prediction/detection, and so on. Thus, there is a need for an efficient classification model that can deal with the EEG datasets more adequately with better classification accuracy, which will further help in developing the automatic solution for the medical domain. In this paper, we have introduced a hybrid classification model for eye state detection using electroencephalogram (EEG) signals. This hybrid classification model has been evaluated with the other traditional machine learning models, eight classification models (Prepossessed + Hypertuned) and six state-of-the-art methods to assess its appropriateness and correctness. This proposed classification model establishes a machine learning-based hybrid model for the classification of eye state using EEG signals with greater exactness. It is also capable of solving the issue of outlier detection and removal to address the class imbalance problem, which will offer the solution toward building the robotic or smart machine-based solution for social well-being.},
}
@article {pmid35125142,
year = {2022},
author = {Del Campo-Vera, RM and Tang, AM and Gogia, AS and Chen, KH and Sebastian, R and Gilbert, ZD and Nune, G and Liu, CY and Kellis, S and Lee, B},
title = {Neuromodulation in Beta-Band Power Between Movement Execution and Inhibition in the Human Hippocampus.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {25},
number = {2},
pages = {232-244},
doi = {10.1111/ner.13486},
pmid = {35125142},
issn = {1525-1403},
mesh = {Adult ; *Electroencephalography ; *Epilepsy/therapy ; Female ; Hippocampus ; Humans ; Male ; Middle Aged ; Movement ; Young Adult ; },
abstract = {INTRODUCTION: The hippocampus is thought to be involved in movement, but its precise role in movement execution and inhibition has not been well studied. Previous work with direct neural recordings has found beta-band (13-30 Hz) modulation in both movement execution and inhibition throughout the motor system, but the role of beta-band modulation in the hippocampus during movement inhibition is not well understood. Here, we perform a Go/No-Go reaching task in ten patients with medically refractory epilepsy to study human hippocampal beta-power changes during movement.<br><br>MATERIALS AND METHODS: Ten epilepsy patients (5 female; ages 21-46) were implanted with intracranial depth electrodes for seizure monitoring and localization. Local field potentials were sampled at 2000 Hz during a Go/No-Go movement task. Comparison of beta-band power between Go and No-Go conditions was conducted using Wilcoxon signed-rank hypothesis testing for each patient. Sub-analyses were conducted to assess differences in the anterior vs posterior contacts, ipsilateral vs contralateral contacts, and male vs female beta-power values.<br><br>RESULTS: Eight out of ten patients showed significant beta-power decreases during the Go movement response (p < 0.05) compared to baseline. Eight out of ten patients also showed significant beta-power increases in the No-Go condition, occurring in the absence of movement. No significant differences were noted between ipsilateral vs contralateral contacts nor in anterior vs posterior hippocampal contacts. Female participants had a higher task success rate than males and had significantly greater beta-power increases in the No-Go condition (p < 0.001).<br><br>CONCLUSION: These findings indicate that increases in hippocampal beta power are associated with movement inhibition. To the best of our knowledge, this study is the first to report this phenomenon in the human hippocampus. The beta band may represent a state-change signal involved in motor processing. Future focus on the beta band in understanding human motor and impulse control will be vital.},
}
@article {pmid35124225,
year = {2022},
author = {Li, G and Jiang, S and Meng, J and Chai, G and Wu, Z and Fan, Z and Hu, J and Sheng, X and Zhang, D and Chen, L and Zhu, X},
title = {Assessing differential representation of hand movements in multiple domains using stereo-electroencephalographic recordings.},
journal = {NeuroImage},
volume = {250},
number = {},
pages = {118969},
doi = {10.1016/j.neuroimage.2022.118969},
pmid = {35124225},
issn = {1095-9572},
mesh = {Adult ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Cues ; Drug Resistant Epilepsy/physiopathology ; Electroencephalography/*methods ; Female ; Hand/*physiology ; Humans ; Male ; Movement/*physiology ; Stereotaxic Techniques ; },
abstract = {Invasive brain-computer interfaces (BCI) have made great progress in the reconstruction of fine hand movement parameters for paralyzed patients, where superficial measurement modalities including electrocorticography (ECoG) and micro-array recordings are mostly used. However, these recording techniques typically focus on the signals from the sensorimotor cortex, leaving subcortical regions and other cortical regions related to the movements largely unexplored. As an intracranial recording technique for the presurgical assessments of brain surgery, stereo-encephalography (SEEG) inserts depth electrodes containing multiple contacts into the brain and thus provides the unique opportunity for investigating movement-related neural representation throughout the brain. Although SEEG samples neural signals with high spatial-temporal resolutions, its potential of being used to build BCIs has just been realized recently, and the decoding of SEEG activity related to hand movements has not been comprehensively investigated yet. Here, we systematically evaluated the factors influencing the performance of movement decoding using SEEG signals recorded from 32 human subjects performing a visually-cued hand movement task. Our results suggest that multiple regions in both lateral and depth directions present significant neural selectivity to the task, whereas the sensorimotor area, including both precentral and postcentral cortex, carries the richest discriminative neural information for the decoding. The posterior parietal and prefrontal cortex contribute gradually less, but still rich sources for extracting movement parameters. The insula, temporal and occipital cortex also contains useful task-related information for decoding. Under the cortex layer, white matter presents decodable neural patterns but yields a lower accuracy (42.0 ± 0.8%) than the cortex on average (44.2 ± 0.8%, p<0.01). Notably, collectively using neural signals from multiple task-related areas can significantly enhance the movement decoding performance by 6.9% (p<0.01) on average compared to using a single region. Among the different spectral components of SEEG activity, the high gamma and delta bands offer the most informative features for hand movements reconstruction. Additionally, the phase-amplitude coupling strength between these two frequency ranges correlates positively with the performance of movement decoding. In the temporal domain, maximum decoding accuracy is first reached around 2 s after the onset of movement commands. In sum, this study provides valuable insights for the future motor BCIs design employing both SEEG recordings and other recording modalities.},
}
@article {pmid35108701,
year = {2022},
author = {Racz, RR and Kollo, M and Racz, G and Bulz, C and Ackels, T and Warner, T and Wray, W and Kiskin, N and Chen, C and Ye, Z and de Hoz, L and Rancz, E and Schaefer, AT},
title = {jULIEs: nanostructured polytrodes for low traumatic extracellular recordings and stimulation in the mammalian brain.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac514f},
pmid = {35108701},
issn = {1741-2552},
support = {FC001153/CRUK_/Cancer Research UK/United Kingdom ; FC001153/WT_/Wellcome Trust/United Kingdom ; MC_UP_1202/5/MRC_/Medical Research Council/United Kingdom ; FC001153/MRC_/Medical Research Council/United Kingdom ; 104285/B/14/Z/WT_/Wellcome Trust/United Kingdom ; 110174/Z/15/Z/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Animals ; *Brain/physiology ; Electric Impedance ; Electrodes, Implanted ; Mice ; Microelectrodes ; Neurons/physiology ; *Silicon Dioxide ; },
abstract = {Objective.Extracellular microelectrode techniques are the most widely used approach to interrogate neuronal populations. However, regardless of the manufacturing method used, damage to the vasculature and circuit function during probe insertion remains a concern. This issue can be mitigated by minimising the footprint of the probe used. Reducing the size of probes typically requires either a reduction in the number of channels present in the probe, or a reduction in the individual channel area. Both lead to less effective coupling between the probe and extracellular signals of interest.Approach.Here, we show that continuously drawn SiO2-insulated ultra-microelectrode fibres offer an attractive substrate to address these challenges. Individual fibres can be fabricated to >10 m continuous stretches and a selection of diameters below 30µm with low resistance (<100 Ω mm-1) continuously conductive metal core of <10µm and atomically flat smooth shank surfaces. To optimize the properties of the miniaturised electrode-tissue interface, we electrodeposit rough Au structures followed by ∼20 nm IrOx film resulting in the reduction of the interfacial impedance to <500 kΩ at 1 kHz.Main results. We demonstrate that these ultra-low impedance electrodes can record and stimulate both single and multi-unit activity with minimal tissue disturbance and exceptional signal-to-noise ratio in both superficial (∼40µm) and deep (∼6 mm) structures of the mouse brain. Further, we show that sensor modifications are stable and probe manufacturing is reproducible.Significance.Minimally perturbing bidirectional neural interfacing can reveal circuit function in the mammalian brainin vivo.},
}
@article {pmid35106254,
year = {2022},
author = {Cripe, CT and Mikulecky, P and Sucher, M and Huang, JH and Hack, D},
title = {Improved Sobriety Rates After Brain-Computer Interface-Based Cognitive Remediation Training.},
journal = {Cureus},
volume = {14},
number = {1},
pages = {e21429},
pmid = {35106254},
issn = {2168-8184},
abstract = {Up to 80% of individuals seeking treatment fail in their attempts at sobriety. This study investigated whether 1) a cognitive remediation therapy (CRT) program augmented with a brain-computer interface (BCI) to influence brain performance metrics would increase participants' self-agency by restoring cognitive control performance; and 2) that ability increase would produce increased sobriety rates, greater than published treatment rates. The study employed a retrospective chart review structured to replicate a switching replication methodology (i.e., waitlist group) using a pre-test and post-test profile analysis quasi-experimental design. Participants' records were organized into treatment and non-treatment groups. Adult poly-substance users were recruited from alcohol and other drugs (AOD) use outpatient programs and AOD use treatment centers in the United States. Participants volunteered for pre- and post-testing without treatment (n = 121) or chose to enter the treatment program (n = 200). The treatment group engaged in a 48-session BCI/CRT augmented treatment program. Pre- and post-treatment measures comprised 14 areas from the Woodcock-Johnson Cognitive Abilities III Assessment Battery. An 18-month follow-up assessment measured maintenance of sobriety. After testing the difference for all variables across time between test groups, a significant multivariate effect was found. In addition, at 18 months post-treatment, 89% of the treatment group maintained sobriety, compared to 31% of the non-treatment group. Consistent with addiction neurobehavioral imbalance models, traditional treatment programs augmented with BCI/CRT training, focused on improving cognitive control abilities, may strengthen self-control and improve sobriety rates.},
}
@article {pmid35104529,
year = {2022},
author = {Jiménez, J and Godinho, R and Pinto, D and Lopes, S and Castro, D and Cubero, D and Osorio, MA and Piqué, J and Moreno-Opo, R and Quiros, P and González-Nuevo, D and Hernandez-Palacios, O and Kéry, M},
title = {The Cantabrian capercaillie: A population on the edge.},
journal = {The Science of the total environment},
volume = {821},
number = {},
pages = {153523},
doi = {10.1016/j.scitotenv.2022.153523},
pmid = {35104529},
issn = {1879-1026},
mesh = {Animals ; DNA ; Female ; *Galliformes ; Humans ; Male ; Population Density ; Population Dynamics ; Spain ; },
abstract = {The capercaillie Tetrao urogallus - the world's largest grouse- is a circumboreal forest species, which only two remaining populations in Spain: one in the Cantabrian mountains in the west and the other in the Pyrenees further east. Both have shown severe declines, especially in the Cantabrian population, which has recently been classified as "Critically Endangered". To develop management plans, information on demographic parameters is necessary to understand and forecast population dynamics. We used spatial capture-recapture (SCR) modeling and non-invasive DNA samples to estimate the current population size in the whole Cantabrian mountain range. In addition, for the assessment of population status, we analyzed the population trajectory over the last 42 years (1978-2019) at 196 leks on the Southern slope of the range, using an integrated population model with a Dail-Madsen model at its core, combined with a multistate capture-recapture model for survival and a Poisson regression for productivity. For 2019, we estimate the size of the entire population at 191 individuals (95% BCI 165-222) for an estimated 60 (48-78) females and 131 (109-157) males. Since the 1970s, our study estimates a shrinkage of the population range by 83%. The population at the studied leks in 2019 was at about 10% of the size estimated for 1978. Apparent annual survival was estimated at 0.707 (0.677-0.735), and per-capita recruitment at 0.233 (0.207-0.262), and insufficient to maintain a stable population. We suggest work to improve the recruitment (and survival) and manage these mountain forests for capercaillie conservation. Also, in the future, management should assess the genetic viability of this population.},
}
@article {pmid35104499,
year = {2022},
author = {Merk, T and Peterson, V and Köhler, R and Haufe, S and Richardson, RM and Neumann, WJ},
title = {Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation.},
journal = {Experimental neurology},
volume = {351},
number = {},
pages = {113993},
doi = {10.1016/j.expneurol.2022.113993},
pmid = {35104499},
issn = {1090-2430},
support = {R01 NS110424/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; *Deep Brain Stimulation ; Machine Learning ; },
abstract = {Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.},
}
@article {pmid35103873,
year = {2022},
author = {Li, Y and Zhong, N and Taniar, D and Zhang, H},
title = {MCGNet+: an improved motor imagery classification based on cosine similarity.},
journal = {Brain informatics},
volume = {9},
number = {1},
pages = {3},
pmid = {35103873},
issn = {2198-4018},
support = {21A13022003//Ministry of Education of the People's Republic of China/ ; LY19F030010//Natural Science Foundation of Zhejiang Province/ ; 20NDJC216YB//Zhejiang Provincial Social Science Fund/ ; 2019A610083//Natural Science Foundation of Ningbo/ ; GH2021642//Zhejiang Provincial Educational Science Scheme 2021/ ; 72071049//National Natural Science Foundation of China/ ; },
abstract = {It has been a challenge for solving the motor imagery classification problem in the brain informatics area. Accuracy and efficiency are the major obstacles for motor imagery analysis in the past decades since the computational capability and algorithmic availability cannot satisfy complex brain signal analysis. In recent years, the rapid development of machine learning (ML) methods has empowered people to tackle the motor imagery classification problem with more efficient methods. Among various ML methods, the Graph neural networks (GNNs) method has shown its efficiency and accuracy in dealing with inter-related complex networks. The use of GNN provides new possibilities for feature extraction from brain structure connection. In this paper, we proposed a new model called MCGNet+, which improves the performance of our previous model MutualGraphNet. In this latest model, the mutual information of the input columns forms the initial adjacency matrix for the cosine similarity calculation between columns to generate a new adjacency matrix in each iteration. The dynamic adjacency matrix combined with the spatial temporal graph convolution network (ST-GCN) has better performance than the unchanged matrix model. The experimental results indicate that MCGNet+ is robust enough to learn the interpretable features and outperforms the current state-of-the-art methods.},
}
@article {pmid35099768,
year = {2022},
author = {Luo, S and Rabbani, Q and Crone, NE},
title = {Brain-Computer Interface: Applications to Speech Decoding and Synthesis to Augment Communication.},
journal = {Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics},
volume = {},
number = {},
pages = {},
pmid = {35099768},
issn = {1878-7479},
support = {UH3NS114439//National Institute of Neurological Disorders and Stroke/ ; U01DC016686//National Institute on Deafness and Other Communication Disorders/ ; },
abstract = {Damage or degeneration of motor pathways necessary for speech and other movements, as in brainstem strokes or amyotrophic lateral sclerosis (ALS), can interfere with efficient communication without affecting brain structures responsible for language or cognition. In the worst-case scenario, this can result in the locked in syndrome (LIS), a condition in which individuals cannot initiate communication and can only express themselves by answering yes/no questions with eye blinks or other rudimentary movements. Existing augmentative and alternative communication (AAC) devices that rely on eye tracking can improve the quality of life for people with this condition, but brain-computer interfaces (BCIs) are also increasingly being investigated as AAC devices, particularly when eye tracking is too slow or unreliable. Moreover, with recent and ongoing advances in machine learning and neural recording technologies, BCIs may offer the only means to go beyond cursor control and text generation on a computer, to allow real-time synthesis of speech, which would arguably offer the most efficient and expressive channel for communication. The potential for BCI speech synthesis has only recently been realized because of seminal studies of the neuroanatomical and neurophysiological underpinnings of speech production using intracranial electrocorticographic (ECoG) recordings in patients undergoing epilepsy surgery. These studies have shown that cortical areas responsible for vocalization and articulation are distributed over a large area of ventral sensorimotor cortex, and that it is possible to decode speech and reconstruct its acoustics from ECoG if these areas are recorded with sufficiently dense and comprehensive electrode arrays. In this article, we review these advances, including the latest neural decoding strategies that range from deep learning models to the direct concatenation of speech units. We also discuss state-of-the-art vocoders that are integral in constructing natural-sounding audio waveforms for speech BCIs. Finally, this review outlines some of the challenges ahead in directly synthesizing speech for patients with LIS.},
}
@article {pmid35095410,
year = {2021},
author = {Papadopoulos, S and Bonaiuto, J and Mattout, J},
title = {An Impending Paradigm Shift in Motor Imagery Based Brain-Computer Interfaces.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {824759},
pmid = {35095410},
issn = {1662-4548},
abstract = {The development of reliable assistive devices for patients that suffer from motor impairments following central nervous system lesions remains a major challenge in the field of non-invasive Brain-Computer Interfaces (BCIs). These approaches are predominated by electroencephalography and rely on advanced signal processing and machine learning methods to extract neural correlates of motor activity. However, despite tremendous and still ongoing efforts, their value as effective clinical tools remains limited. We advocate that a rather overlooked research avenue lies in efforts to question neurophysiological markers traditionally targeted in non-invasive motor BCIs. We propose an alternative approach grounded by recent fundamental advances in non-invasive neurophysiology, specifically subject-specific feature extraction of sensorimotor bursts of activity recorded via (possibly magnetoencephalography-optimized) electroencephalography. This path holds promise in overcoming a significant proportion of existing limitations, and could foster the wider adoption of online BCIs in rehabilitation protocols.},
}
@article {pmid35094982,
year = {2022},
author = {Wu, X and Zheng, WL and Li, Z and Lu, BL},
title = {Investigating EEG-based functional connectivity patterns for multimodal emotion recognition.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac49a7},
pmid = {35094982},
issn = {1741-2552},
mesh = {Arousal ; Brain ; *Electroencephalography ; Emotions ; *Neural Networks, Computer ; },
abstract = {Objective.Previous studies on emotion recognition from electroencephalography (EEG) mainly rely on single-channel-based feature extraction methods, which ignore the functional connectivity between brain regions. Hence, in this paper, we propose a novel emotion-relevant critical subnetwork selection algorithm and investigate three EEG functional connectivity network features: strength, clustering coefficient, and eigenvector centrality.Approach.After constructing the brain networks by the correlations between pairs of EEG signals, we calculated critical subnetworks through the average of brain network matrices with the same emotion label to eliminate the weak associations. Then, three network features were conveyed to a multimodal emotion recognition model using deep canonical correlation analysis along with eye movement features. The discrimination ability of the EEG connectivity features in emotion recognition is evaluated on three public datasets: SEED, SEED-V, and DEAP.Main results. The experimental results reveal that the strength feature outperforms the state-of-the-art features based on single-channel analysis. The classification accuracies of multimodal emotion recognition are95.08±6.42%on the SEED dataset,84.51±5.11%on the SEED-V dataset, and85.34±2.90%and86.61±3.76%for arousal and valence on the DEAP dataset, respectively, which all achieved the best performance. In addition, the brain networks constructed with 18 channels achieve comparable performance with that of the 62-channel network and enable easier setups in real scenarios.Significance.The EEG functional connectivity networks combined with emotion-relevant critical subnetworks selection algorithm we proposed is a successful exploration to excavate the information between channels.},
}
@article {pmid35093844,
year = {2022},
author = {Sadiq, MT and Aziz, MZ and Almogren, A and Yousaf, A and Siuly, S and Rehman, AU},
title = {Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework.},
journal = {Computers in biology and medicine},
volume = {143},
number = {},
pages = {105242},
doi = {10.1016/j.compbiomed.2022.105242},
pmid = {35093844},
issn = {1879-0534},
abstract = {Identifying motor and mental imagery electroencephalography (EEG) signals is imperative to realizing automated, robust brain-computer interface (BCI) systems. In the present study, we proposed a pretrained convolutional neural network (CNN)-based new automated framework feasible for robust BCI systems with small and ample samples of motor and mental imagery EEG training data. The framework is explored by investigating the implications of different limiting factors, such as learning rates and optimizers, processed versus unprocessed scalograms, and features derived from untuned pretrained models in small, medium, and large pretrained CNN models. The experiments were performed on three public datasets obtained from BCI Competition III. The datasets were denoised with multiscale principal component analysis, and time-frequency scalograms were obtained by employing a continuous wavelet transform. The scalograms were fed into several variants of ten pretrained models for feature extraction and identification of different EEG tasks. The experimental results showed that ShuffleNet yielded the maximum average classification accuracy of 99.52% using an RMSProp optimizer with a learning rate of 0.000 1. It was observed that low learning rates converge to more optimal performances compared to high learning rates. Moreover, noisy scalograms and features extracted from untuned networks resulted in slightly lower performance than denoised scalograms and tuned networks, respectively. The overall results suggest that pretrained models are robust when identifying EEG signals because of their ability to preserve the time-frequency structure of EEG signals and promising classification outcomes.},
}
@article {pmid35092360,
year = {2022},
author = {Kiba, K and Akashi, Y and Yamamoto, Y and Hirayama, A and Fujimoto, K and Uemura, H},
title = {Clinical features of detrusor underactivity in elderly men without neurological disorders.},
journal = {Lower urinary tract symptoms},
volume = {},
number = {},
pages = {},
doi = {10.1111/luts.12424},
pmid = {35092360},
issn = {1757-5672},
abstract = {OBJECTIVES: To investigate the clinical features of detrusor underactivity (DU) in elderly men without neurological disorders.<br><br>METHODS: A total of 336 men aged ≥50 years without neurogenic disorders who underwent pressure flow studies and who had DU or bladder outlet obstruction (BOO) were reviewed retrospectively. According to the bladder contractility index (BCI) and the BOO index (BOOI), the subjects were classified into the following three groups: (a) pure DU group, BCI < 100 and BOOI < 40; (b) DU + BOO group, BCI < 100 and BOOI ≥ 40; and (c) pure BOO group, BCI ≥ 100 and BOOI ≥ 40. Subjective and objective parameters were compared among the three groups, and the predictors for pure DU were evaluated by multivariate analysis.<br><br>RESULTS: Of the 336 patients, 205 who met the study criteria were included in the analysis: 63 (30.7%) with pure DU, 48 (23.4%) with DU + BOO, and 94 (45.9%) with pure BOO. The proportion of the pure DU group increased with increasing age. Prostate volume was the lowest in the pure DU group. Frequency, urgency on the International Prostate Symptom Score (IPSS), and the IPSS storage subscore were the lowest in the pure DU group. Multivariate analysis showed that age (odds ratio [OR] 1.114 [95% CI, 1.032-1.203], P = .005), prostate volume (OR 0.968 [95% CI, 0.949-0.987], P = .001), and urgency (OR 0.623 [95% CI, 0.431-0.900], P = .012) were predictors of pure DU.<br><br>CONCLUSION: Older age, smaller prostate volume, and less urgency may be clinical features of pure DU.},
}
@article {pmid35090904,
year = {2022},
author = {Cai, Y and She, Q and Ji, J and Ma, Y and Zhang, J and Zhang, Y},
title = {Motor imagery EEG decoding using manifold embedded transfer learning.},
journal = {Journal of neuroscience methods},
volume = {370},
number = {},
pages = {109489},
doi = {10.1016/j.jneumeth.2022.109489},
pmid = {35090904},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Learning ; Machine Learning ; },
abstract = {BACKGROUND: Brain computer interface (BCI) utilizes brain signals to help users interact with external devices directly. EEG is one of the most commonly used techniques for brain signal acquisition in BCI. However, it is notoriously difficult to build a generic EEG recognition model due to significant non-stationarity and subject-to-subject variations, and the requirement for long time training. Transfer learning (TL) is particularly useful because it can alleviate the calibration requirement in EEG-based BCI applications by transferring the calibration information from existing subjects to new subject. To take advantage of geometric properties in Riemann manifold and joint distribution adaptation, a manifold embedded transfer learning (METL) framework was proposed for motor imagery (MI) EEG decoding.<br><br>NEW METHOD: First, the covariance matrices of the EEG trials are first aligned on the SPD manifold. Then the features are extracted from both the symmetric positive definite (SPD) manifold and Grassmann manifold. Finally, the classification model is learned by combining the structural risk minimization (SRM) of source domain and joint distribution alignment of source and target domains.<br><br>RESULT: Experimental results on two MI EEG datasets verify the effectiveness of the proposed METL. In particular, when there are a small amount of labeled samples in the target domain, METL demonstrated a more accurate and stable classification performance than conventional methods.<br><br>Compared with several state-of-the-art methods, METL has achieved better classification accuracy, 71.81% and 69.06% in single-to-single (STS), 83.14% and 76.00% in multi-to-single (MTS) transfer tasks, respectively.<br><br>CONCLUSIONS: METL can cope with single source domain or multi-source domains and compared with single-source transfer learning, multi-source transfer learning can improve the performance effectively due to the data expansion. It is effective enough to achieve superior performance for classification of EEG signals.},
}
@article {pmid35088740,
year = {2022},
author = {Suzuki, Y and Jovanovic, LI and Fadli, RA and Yamanouchi, Y and Marquez-Chin, C and Popovic, MR and Nomura, T and Milosevic, M},
title = {Evidence That Brain-Controlled Functional Electrical Stimulation Could Elicit Targeted Corticospinal Facilitation of Hand Muscles in Healthy Young Adults.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neurom.2021.12.007},
pmid = {35088740},
issn = {1525-1403},
abstract = {OBJECTIVES: Brain-computer interface (BCI)-controlled functional electrical stimulation (FES) has been used in rehabilitation for improving hand motor function. However, mechanisms of improvements are still not well understood. The objective of this study was to investigate how BCI-controlled FES affects hand muscle corticospinal excitability.<br><br>MATERIALS AND METHODS: A total of 12 healthy young adults were recruited in the study. During BCI calibration, a single electroencephalography channel from the motor cortex and a frequency band were chosen to detect event-related desynchronization (ERD) of cortical oscillatory activity during kinesthetic wrist motor imagery (MI). The MI-based BCI system was used to detect active states on the basis of ERD activity in real time and produce contralateral wrist extension movements through FES of the extensor carpi radialis (ECR) muscle. As a control condition, FES was used to generate wrist extension at random intervals. The two interventions were performed on separate days and lasted 25 minutes. Motor evoked potentials (MEPs) in ECR (intervention target) and flexor carpi radialis (FCR) muscles were elicited through single-pulse transcranial magnetic stimulation of the motor cortex to compare corticospinal excitability before (pre), immediately after (post0), and 30 minutes after (post30) the interventions.<br><br>RESULTS: After the BCI-FES intervention, ECR muscle MEPs were significantly facilitated at post0 and post30 time points compared with before the intervention (pre), whereas there were no changes in the FCR muscle corticospinal excitability. Conversely, after the random FES intervention, both ECR and FCR muscle MEPs were unaffected compared with before the intervention (pre).<br><br>CONCLUSIONS: Our results demonstrated evidence that BCI-FES intervention could elicit muscle-specific short-term corticospinal excitability facilitation of the intervention targeted (ECR) muscle only, whereas randomly applied FES was ineffective in eliciting any changes. Notably, these findings suggest that associative cortical and peripheral activations during BCI-FES can effectively elicit targeted muscle corticospinal excitability facilitation, implying possible rehabilitation mechanisms.},
}
@article {pmid35087788,
year = {2021},
author = {Xue, X and Yang, X and Deng, Z and Tu, H and Kong, D and Li, N and Xu, F},
title = {Global Trends and Hotspots in Research on Rehabilitation Robots: A Bibliometric Analysis From 2010 to 2020.},
journal = {Frontiers in public health},
volume = {9},
number = {},
pages = {806723},
pmid = {35087788},
issn = {2296-2565},
mesh = {Artificial Intelligence ; Bibliometrics ; Databases, Factual ; Humans ; Reproducibility of Results ; *Robotics ; United States ; },
abstract = {Background: In recent years, with the development of medical science and artificial intelligence, research on rehabilitation robots has gained more and more attention, for nearly 10 years in the Web of Science database by journal of rehabilitation robot-related research literature analysis, to parse and track rehabilitation robot research hotspot and front, and provide some guidance for future research. Methods: This study employed computer retrieval of rehabilitation robot-related research published in the core data collection of the Web of Science database from 2010 to 2020, using CiteSpace 5.7 visualization software. The hotspots and frontiers of rehabilitation robot research are analyzed from the aspects of high-influence countries or regions, institutions, authors, high-frequency keywords, and emergent words. Results: A total of 3,194 articles were included. In recent years, the research on rehabilitation robots has been continuously hot, and the annual publication of relevant literature has shown a trend of steady growth. The United States ranked first with 819 papers, and China ranked second with 603 papers. Northwestern University ranked first with 161 publications. R. Riener, a professor at the University of Zurich, Switzerland, ranked as the first author with 48 articles. The Journal of Neural Engineering and Rehabilitation has the most published research, with 211 publications. In the past 10 years, research has focused on intelligent control, task analysis, and the learning, performance, and reliability of rehabilitation robots to realize the natural and precise interaction between humans and machines. Research on neural rehabilitation robots, brain-computer interface, virtual reality, flexible wearables, task analysis, and exoskeletons has attracted more and more attention. Conclusions: At present, the brain-computer interface, virtual reality, flexible wearables, task analysis, and exoskeleton rehabilitation robots are the research trends and hotspots. Future research should focus on the application of machine learning (ML), dimensionality reduction, and feature engineering technologies in the research and development of rehabilitation robots to improve the speed and accuracy of algorithms. To achieve wide application and commercialization, future rehabilitation robots should also develop toward mass production and low cost. We should pay attention to the functional needs of patients, strengthen multidisciplinary communication and cooperation, and promote rehabilitation robots to better serve the rehabilitation medical field.},
}
@article {pmid35087580,
year = {2022},
author = {Song, X and Zeng, Y and Tong, L and Shu, J and Yang, Q and Kou, J and Sun, M and Yan, B},
title = {A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual Targets.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {4752450},
pmid = {35087580},
issn = {1687-5273},
mesh = {*Brain-Computer Interfaces ; Learning ; },
abstract = {The superiority of collaborative brain-computer interface (cBCI) in performance enhancement makes it an effective way to break through the performance bottleneck of the BCI-based dynamic visual target detection. However, the existing cBCIs focus on multi-mind information fusion with a static and unidirectional mode, lacking the information interaction and learning guidance among multiple agents. Here, we propose a novel cBCI framework to enhance the group detection performance of dynamic visual targets. Specifically, a mutual learning domain adaptation network (MLDANet) with information interaction, dynamic learning, and individual transferring abilities is developed as the core of the cBCI framework. MLDANet takes P3-sSDA network as individual network unit, introduces mutual learning strategy, and establishes a dynamic interactive learning mechanism between individual networks and collaborative decision-making at the neural decision level. The results indicate that the proposed MLDANet-cBCI framework can achieve the best group detection performance, and the mutual learning strategy can improve the detection ability of individual networks. In MLDANet-cBCI, the F1 scores of collaborative detection and individual network are 0.12 and 0.19 higher than those in the multi-classifier cBCI, respectively, when three minds collaborate. Thus, the proposed framework breaks through the traditional multi-mind collaborative mode and exhibits a superior group detection performance of dynamic visual targets, which is also of great significance for the practical application of multi-mind collaboration.},
}
@article {pmid35087399,
year = {2021},
author = {Wen, D and Xu, J and Wu, Z and Liu, Y and Zhou, Y and Li, J and Wang, S and Dong, X and Saripan, MI and Song, H},
title = {The Effective Cognitive Assessment and Training Methods for COVID-19 Patients With Cognitive Impairment.},
journal = {Frontiers in aging neuroscience},
volume = {13},
number = {},
pages = {827273},
pmid = {35087399},
issn = {1663-4365},
}
@article {pmid35085095,
year = {2022},
author = {Fang, H and Jin, J and Daly, I and Wang, XY},
title = {Feature Extraction Method Based on Filter Banks and Riemannian Tangent Space in Motor-Imagery BCI.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2022.3146274},
pmid = {35085095},
issn = {2168-2208},
abstract = {Optimal feature extraction for multi-category motor imagery brain-computer interfaces (MI-BCIs) is a research hotspot. The common spatial pattern (CSP) algorithm is one of the most widely used methods in MI-BCIs. However, its performance is adversely affected by variance in the operational frequency band and noise interference. Furthermore, the performance of CSP is not satisfactory when addressing multi-category classification problems. In this work, we propose a fusion method combining Filter Banks and Riemannian Tangent Space (FBRTS) in multiple time windows. FBRTS uses multiple filter banks to overcome the problem of variance in the operational frequency band. It also applies the Riemannian method to the covariance matrix extracted by the spatial filter to obtain more robust features in order to overcome the problem of noise interference. In addition, we use a One-Versus-Rest support vector machine (OVR-SVM) model to classify multi-category features. We evaluate our FBRTS method using BCI competition IV dataset 2a and 2b. The experimental results show that the average classification accuracy of our FBRTS method is 77.7% and 86.9% in datasets 2a and 2b respectively. By analyzing the influence of the different numbers of filter banks and time windows on the performance of our FBRTS method, we can identify the optimal number of filter banks and time windows. Additionally, our FBRTS method can obtain more distinctive features than the filter banks common spatial pattern (FBCSP) method in two-dimensional embedding space. These results show that our proposed method can improve the performance of MI-BCIs.},
}
@article {pmid35083329,
year = {2022},
author = {Sahoo, SK and Mohapatra, SK},
title = {Recognition of Ocular Artifacts in EEG Signal through a Hybrid Optimized Scheme.},
journal = {BioMed research international},
volume = {2022},
number = {},
pages = {4875399},
pmid = {35083329},
issn = {2314-6141},
mesh = {Algorithms ; *Artifacts ; Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Wavelet Analysis ; },
abstract = {Brain computer interface (BCI) requires an online and real-time processing of EEG signals. Hence, the accuracy of the recording system is improved by nullifying the developed artifacts. The goal of this proposal is to develop a hybrid model for recognizing and minimizing ocular artifacts through an improved deep learning scheme. The discrete wavelet transform (DWT) and Pisarenko harmonic decomposition are used for decomposing the signals. Then, the features are extracted by principal component analysis (PCA) and independent component analysis (ICA) techniques. After collecting the features, an optimized deformable convolutional network (ODCN) is used for the recognition of ocular artifacts from EEG input signals. When artifacts are sensed, the moderation method is executed by applying the empirical mean curve decomposition (EMCD) followed by ODCN for noise optimization in EEG signals. Conclusively, the spotless signal is reconstructed by an application of inverse EMCD. The proposed method has achieved a higher performance than that of conventional methods, which demonstrates a better ocular artifact reduction by the proposed method.},
}
@article {pmid35081400,
year = {2022},
author = {Reiss, JD and Peterson, LS and Nesamoney, SN and Chang, AL and Pasca, AM and Marić, I and Shaw, GM and Gaudilliere, B and Wong, RJ and Sylvester, KG and Bonifacio, SL and Aghaeepour, N and Gibbs, RS and Stevenson, DK},
title = {Perinatal infection, inflammation, preterm birth, and brain injury: A review with proposals for future investigations.},
journal = {Experimental neurology},
volume = {351},
number = {},
pages = {113988},
doi = {10.1016/j.expneurol.2022.113988},
pmid = {35081400},
issn = {1090-2430},
abstract = {Preterm newborns are exposed to several risk factors for developing brain injury. Clinical studies have suggested that the presence of intrauterine infection is a consistent risk factor for preterm birth and white matter injury. Animal models have confirmed these associations by identifying inflammatory cascades originating at the maternofetal interface that penetrate the fetal blood-brain barrier and result in brain injury. Acquired diseases of prematurity further potentiate the risk for cerebral injury. Systems biology approaches incorporating ante- and post-natal risk factors and analyzing omic and multiomic data using machine learning are promising methodologies for further elucidating biologic mechanisms of fetal and neonatal brain injury.},
}
@article {pmid35078639,
year = {2022},
author = {Gallego, JA and Makin, TR and McDougle, SD},
title = {Going beyond primary motor cortex to improve brain-computer interfaces.},
journal = {Trends in neurosciences},
volume = {45},
number = {3},
pages = {176-183},
doi = {10.1016/j.tins.2021.12.006},
pmid = {35078639},
issn = {1878-108X},
support = {715022/ERC_/European Research Council/International ; /WT_/Wellcome Trust/United Kingdom ; },
mesh = {Brain ; *Brain-Computer Interfaces ; Humans ; *Motor Cortex ; Movement ; },
abstract = {Brain-computer interfaces (BCIs) for movement restoration typically decode the user's intent from neural activity in their primary motor cortex (M1) and use this information to enable 'mental control' of an external device. Here, we argue that activity in M1 has both too little and too much information for optimal decoding: too little, in that many regions beyond it contribute unique motor outputs and have movement-related information that is absent or otherwise difficult to resolve from M1 activity; and too much, in that motor commands are tangled up with nonmotor processes such as attention and feedback processing, potentially hindering decoding. Both challenges might be circumvented, we argue, by integrating additional information from multiple brain regions to develop BCIs that will better interpret the user's intent.},
}
@article {pmid35078158,
year = {2022},
author = {Fang, T and Song, Z and Zhan, G and Zhang, X and Mu, W and Wang, P and Zhang, L and Kang, X},
title = {Decoding motor imagery tasks using ESI and hybrid feature CNN.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac4ed0},
pmid = {35078158},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination/physiology ; Neural Networks, Computer ; Wavelet Analysis ; },
abstract = {Objective.Brain-computer interface (BCI) based on motor imaging electroencephalogram (MI-EEG) can be useful in a natural interaction system. In this paper, a new framework is proposed to solve the MI-EEG binary classification problem.Approach.Electrophysiological source imaging (ESI) technology is used to solve the influence of volume conduction effect and improve spatial resolution. Continuous wavelet transform and best time of interest (TOI) are combined to extract the optimal discriminant spatial-frequency features. Finally, a convolutional neural network with seven convolution layers is used to classify the features. In addition, we also validated several new data augment methods to solve the problem of small data sets and reduce network over-fitting.Main results.The model achieved an average classification accuracy of 93.2% and 95.4% on the BCI Competition III IVa and high-gamma data sets, which is better than most of the published advanced algorithms. By selecting the best TOI for each subject, the classification accuracy rate increased by about 2%. The effects of four data augment methods on the classification results were also verified. Among them, the noise addition and overlap methods are better than the other two, and the classification accuracy is improved by at least 4%. On the contrary, the rotation and flip data augment methods reduced the classification accuracy.Significance.Decoding MI tasks can benefit from combing the ESI technology and the data augment technology, which is used to solve the problem of low spatial resolution and small samples of EEG signals, respectively. Based on the results, the model proposed has higher accuracy and application potential in the task of MI-EEG binary classification.},
}
@article {pmid35078156,
year = {2022},
author = {Yao, L and Zhu, B and Shoaran, M},
title = {Fast and accurate decoding of finger movements from ECoG through Riemannian features and modern machine learning techniques.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac4ed1},
pmid = {35078156},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Electrocorticography/methods ; Electroencephalography/methods ; Fingers ; Humans ; Machine Learning ; Movement ; },
abstract = {Objective.Accurate decoding of individual finger movements is crucial for advanced prosthetic control. In this work, we introduce the use of Riemannian-space features and temporal dynamics of electrocorticography (ECoG) signal combined with modern machine learning (ML) tools to improve the motor decoding accuracy at the level of individual fingers.Approach.We selected a set of informative biomarkers that correlated with finger movements and evaluated the performance of state-of-the-art ML algorithms on the brain-computer interface (BCI) competition IV dataset (ECoG, three subjects) and a second ECoG dataset with a similar recording paradigm (Stanford, nine subjects). We further explored the temporal concatenation of features to effectively capture the history of ECoG signal, which led to a significant improvement over single-epoch decoding in both classification (p < 0.01) and regression tasks (p < 0.01).Main results.Using feature concatenation and gradient boosted trees (the top-performing model), we achieved a classification accuracy of 77.0% in detecting individual finger movements (six-class task, including rest state), improving over the state-of-the-art conditional random fields by 11.7% on the three BCI competition subjects. In continuous decoding of movement trajectory, our approach resulted in an average Pearson's correlation coefficient (r) of 0.537 across subjects and fingers, outperforming both the BCI competition winner and the state-of-the-art approach reported on the same dataset (CNN + LSTM). Furthermore, our proposed method features a low time complexity, with only<17.2 s required for training and<50 ms for inference. This enables about 250× speed-up in training compared to previously reported deep learning method with state-of-the-art performance.Significance.The proposed techniques enable fast, reliable, and high-performance prosthetic control through minimally-invasive cortical signals.},
}
@article {pmid35075067,
year = {2022},
author = {Pino, O},
title = {A randomized controlled trial (RCT) to explore the effect of audio-visual entrainment among psychological disorders.},
journal = {Acta bio-medica : Atenei Parmensis},
volume = {92},
number = {6},
pages = {e2021408},
pmid = {35075067},
issn = {2531-6745},
mesh = {Adaptation, Psychological ; Anxiety ; Emotions ; Humans ; *Mental Disorders ; Middle Aged ; },
abstract = {BACKGROUND AND AIM: Although many mental disorders have relevant proud in neurobiological dysfunctions, most intervention approaches neglect neurophysiological features or use pharmacological intervention alone. Non-invasive Brain-Computer Interfaces (BCIs), providing natural ways of modulating mood states, can be promoted as an alternative intervention to cope with neurobiological dysfunction.<br><br>METHODS: A BCI prototype was proposed to feedback a person's affective state such that a closed-loop interaction between the participant's brain responses and the musical stimuli is established. It feedbacks in real-time flickering lights matching with the individual's brain rhythms undergo to auditory stimuli. A RCT was carried out on 15 individuals of both genders (mean age = 49.27 years) with anxiety and depressive spectrum disorders randomly assigned to 2 groups (experimental vs. active control).<br><br>RESULTS: Outcome measures revealed either a significant decrease in Hamilton Rating Scale for Depression (HAM-D) scores and gains in cognitive functions only for participants who undergone to the experimental treatment. Variability in HAM-D scores seems explained by the changes in beta 1, beta 2 and delta bands. Conversely, the rise in cognitive function scores appear associated with theta variations.<br><br>CONCLUSIONS: Future work needs to validate the relationship proposed here between music and brain responses. Findings of the present study provided support to a range of research examining BCI brain modulation and contributes to the understanding of this technique as instruments to alternative therapies We believe that Neuro-Upper can be used as an effective new tool for investigating affective responses, and emotion regulation (www.actabiomedica.it).},
}
@article {pmid35074827,
year = {2022},
author = {García Murillo, D and Zhao, Y and Rogovin, OS and Zhang, K and Hu, AW and Kim, MR and Chen, S and Wang, Z and Keeley, ZC and Shin, DI and Suárez Casanova, VM and Zhu, Y and Martin, L and Papaemmanouil, O and Van Hooser, SD},
title = {NDI: A Platform-Independent Data Interface and Database for Neuroscience Physiology and Imaging Experiments.},
journal = {eNeuro},
volume = {9},
number = {1},
pages = {},
pmid = {35074827},
issn = {2373-2822},
support = {R24 MH114678/MH/NIMH NIH HHS/United States ; },
mesh = {Ecosystem ; *Information Storage and Retrieval ; *Neurosciences ; Software ; Vocabulary ; },
abstract = {Collaboration in neuroscience is impeded by the difficulty of sharing primary data, results, and software across labs. Here, we introduce Neuroscience Data Interface (NDI), a platform-independent standard that allows an analyst to use and create software that functions independently from the format of the raw data or the manner in which the data are organized into files. The interface is rooted in a simple vocabulary that describes common apparatus and storage devices used in neuroscience experiments. Results of analyses, and analyses of analyses, are stored as documents in a scalable, queryable database that stores the relationships and history among the experiment elements and documents. The interface allows the development of an application ecosystem where applications can focus on calculation rather than data format or organization. This tool can be used by individual labs to exchange and analyze data, and it can serve to curate neuroscience data for searchable archives.},
}
@article {pmid35073648,
year = {2022},
author = {, and , },
title = {[Chinese expert consensus on multigene testing for postoperatively adjuvant treatment of hormone receptor-positive, HER2-negative early breast cancer].},
journal = {Zhonghua zhong liu za zhi [Chinese journal of oncology]},
volume = {44},
number = {1},
pages = {54-59},
doi = {10.3760/cma.j.cn112152-20211108-00822},
pmid = {35073648},
issn = {0253-3766},
support = {82172650//National Natural Science Foundation of China/ ; 12019XK320071//Foundation for Clinical Translational and Medical Research, Chinese Academy of Medical Sciences/ ; YXJL-2020-0941-0763//Beijing Medical Award Foundatin/ ; },
mesh = {*Breast Neoplasms/drug therapy/genetics ; Chemotherapy, Adjuvant ; China ; Consensus ; Female ; Hormones/therapeutic use ; Humans ; Prognosis ; Receptor, ErbB-2/genetics ; },
abstract = {Breast cancer is the most common malignant tumor in women, of which early-stage (stages Ⅰ-Ⅱ) breast cancer (EBC) accounts for 73.1%. The strategy of postoperative adjuvant treatment relies mainly on the clinicopathologic characteristics of patients, but there are certain deficiencies in the assessment of treatment benefits and disease prognosis. Multigene testing tools can evaluate the prognosis and predict therapeutic effects of breast cancer patients to guide the clinical decision-making on whether to use adjuvant chemotherapy, radiotherapy, and endocrine therapy by detecting the expression levels of specific genes. The consensus-writing expert group, based on the characteristics, validation results, and accessibility of the multigene testing tools and combined with clinical practice, described the result interpretation and clinical application of OncotypeDx(®) (21-gene), MammaPrint(®) (70-gene), RecurIndex(®) (28-gene), and BreastCancerIndex(®) (BCI, 7-gene) for hormone receptor-positive and human epidermal growth factor receptor 2-negative EBC. The development and validation process of each tool was also briefly introduced. It is expected that the consensus will help to guide and standardize the clinical application of multigene testing tools and further improve the level of precise treatment for EBC.},
}
@article {pmid35073267,
year = {2022},
author = {Xu, M and Chen, Y and Wang, Y and Wang, D and Liu, Z and Zhang, L},
title = {BWGAN-GP: An EEG Data Generation Method for Class Imbalance Problem in RSVP Tasks.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {251-263},
doi = {10.1109/TNSRE.2022.3145515},
pmid = {35073267},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Calibration ; *Electroencephalography ; Humans ; },
abstract = {In the rapid serial visual presentation (RSVP) classification task, the data from the target and non-target classes are incredibly imbalanced. These class imbalance problems (CIPs) can hinder the classifier from achieving better performance, especially in deep learning. This paper proposed a novel data augmentation method called balanced Wasserstein generative adversarial network with gradient penalty (BWGAN-GP) to generate RSVP minority class data. The model learned useful features from majority classes and used them to generate minority-class artificial EEG data. It combines generative adversarial network (GAN) with autoencoder initialization strategy enables this method to learn an accurate class-conditioning in the latent space to drive the generation process towards the minority class. We used RSVP datasets from nine subjects to evaluate the classification performance of our proposed generated model and compare them with those of other methods. The average AUC obtained with BWGAN-GP on EEGNet was 94.43%, an increase of 3.7% over the original data. We also used different amounts of original data to investigate the effect of the generated EEG data on the calibration phase. Only 60% of original data were needed to achieve acceptable classification performance. These results show that the BWGAN-GP could effectively alleviate CIPs in the RSVP task and obtain the best performance when the two classes of data are balanced. The findings suggest that data augmentation techniques could generate artificial EEG to reduce calibration time in other brain-computer interfaces (BCI) paradigms similar to RSVP.},
}
@article {pmid35072767,
year = {2022},
author = {Talon, E and Wimmer, W and Hakim, A and Kiefer, C and Pastore-Wapp, M and Anschuetz, L and Mantokoudis, G and Caversaccio, MD and Wagner, F},
title = {Influence of head orientation and implantation site of a novel transcutaneous bone conduction implant on MRI metal artifact reduction sequence.},
journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery},
volume = {},
number = {},
pages = {},
pmid = {35072767},
issn = {1434-4726},
abstract = {PURPOSE: The use of magnetic resonance imaging (MRI) is often limited in patients with auditory implants because of the presence of metallic components and magnets. The aim of this study was to evaluate the clinical usefulness of a customized MRI sequence for metal artifact suppression in patients with BONEBRIDGETM BCI 602 implants (MED-EL, Innsbruck, Austria), the successor of the BCI 601 model.<br><br>METHODS: Using our in-house developed and customized metal artifact reduction sequence (SEMAC-VAT WARP), MRI artifacts were evaluated qualitatively and quantitatively. MRI sequences were performed with and without artifact reduction on two whole head specimens with and without the BCI 602 implant. In addition, the influence of two different implantation sites (mastoid versus retrosigmoid) and head orientation on artifact presence was investigated.<br><br>RESULTS: Artifact volume was reduced by more than the 50%. Results were comparable with those obtained with the BCI 601, showing no significant differences in the dimensions of artifacts caused by the implant.<br><br>CONCLUSION: SEMAC-VAT WARP was once more proved to be efficient at reducing metal artifacts on MR images. The dimensions of artifacts associated with the BCI 602 are not smaller than those caused by the BCI 601.},
}
@article {pmid35069721,
year = {2022},
author = {Abdi Alkareem Alyasseri, Z and Alomari, OA and Al-Betar, MA and Awadallah, MA and Hameed Abdulkareem, K and Abed Mohammed, M and Kadry, S and Rajinikanth, V and Rho, S},
title = {EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {5974634},
pmid = {35069721},
issn = {1687-5273},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Delivery of Health Care ; Electrodes ; *Electroencephalography ; Humans ; },
abstract = {Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain's electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86% using only 24 sensors with AR20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. Finally, the recommended approach can draw future directions to be applied to different research areas.},
}
@article {pmid35069099,
year = {2021},
author = {Salari, V and Rodrigues, S and Saglamyurek, E and Simon, C and Oblak, D},
title = {Are Brain-Computer Interfaces Feasible With Integrated Photonic Chips?.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {780344},
pmid = {35069099},
issn = {1662-4548},
abstract = {The present paper examines the viability of a radically novel idea for brain-computer interface (BCI), which could lead to novel technological, experimental, and clinical applications. BCIs are computer-based systems that enable either one-way or two-way communication between a living brain and an external machine. BCIs read-out brain signals and transduce them into task commands, which are performed by a machine. In closed loop, the machine can stimulate the brain with appropriate signals. In recent years, it has been shown that there is some ultraweak light emission from neurons within or close to the visible and near-infrared parts of the optical spectrum. Such ultraweak photon emission (UPE) reflects the cellular (and body) oxidative status, and compelling pieces of evidence are beginning to emerge that UPE may well play an informational role in neuronal functions. In fact, several experiments point to a direct correlation between UPE intensity and neural activity, oxidative reactions, EEG activity, cerebral blood flow, cerebral energy metabolism, and release of glutamate. Therefore, we propose a novel skull implant BCI that uses UPE. We suggest that a photonic integrated chip installed on the interior surface of the skull may enable a new form of extraction of the relevant features from the UPE signals. In the current technology landscape, photonic technologies are advancing rapidly and poised to overtake many electrical technologies, due to their unique advantages, such as miniaturization, high speed, low thermal effects, and large integration capacity that allow for high yield, volume manufacturing, and lower cost. For our proposed BCI, we are making some very major conjectures, which need to be experimentally verified, and therefore we discuss the controversial parts, feasibility of technology and limitations, and potential impact of this envisaged technology if successfully implemented in the future.},
}
@article {pmid35069096,
year = {2021},
author = {Han, C and Xu, G and Zheng, X and Tian, P and Zhang, K and Yan, W and Jia, Y and Chen, X},
title = {Assessing the Effect of the Refresh Rate of a Device on Various Motion Stimulation Frequencies Based on Steady-State Motion Visual Evoked Potentials.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {757679},
pmid = {35069096},
issn = {1662-4548},
abstract = {The refresh rate is one of the important parameters of visual presentation devices, and assessing the effect of the refresh rate of a device on motion perception has always been an important direction in the field of visual research. This study examined the effect of the refresh rate of a device on the motion perception response at different stimulation frequencies and provided an objective visual electrophysiological assessment method for the correct selection of display parameters in a visual perception experiment. In this study, a flicker-free steady-state motion visual stimulation with continuous scanning frequency and different forms (sinusoidal or triangular) was presented on a low-latency LCD monitor at different refresh rates. Seventeen participants were asked to observe the visual stimulation without head movement or eye movement, and the effect of the refresh rate was assessed by analyzing the changes in the intensity of their visual evoked potentials. The results demonstrated that an increased refresh rate significantly improved the intensity of motion visual evoked potentials at stimulation frequency ranges of 7-28 Hz, and there was a significant interaction between the refresh rate and motion frequency. Furthermore, the increased refresh rate also had the potential to enhance the ability to perceive similar motion. Therefore, we recommended using a refresh rate of at least 120 Hz in motion visual perception experiments to ensure a better stimulation effect. If the motion frequency or velocity is high, a refresh rate of≥240 Hz is also recommended.},
}
@article {pmid35064439,
year = {2022},
author = {Sun, J and Wei, M and Luo, N and Li, Z and Wang, H},
title = {Euler common spatial patterns for EEG classification.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {60},
number = {3},
pages = {753-767},
pmid = {35064439},
issn = {1741-0444},
support = {61773114//National Natural Science Foundation of China/ ; GXXT-2020-015//the university synergy innovation program of anhui province/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {The technique of common spatial patterns (CSP) is a widely used method in the field of feature extraction of electroencephalogram (EEG) signals. Motivated by the fact that a cosine distance can enlarge the distance between samples of different classes, we propose the Euler CSP (e-CSP) for the feature extraction of EEG signals, and it is then used for EEG classification. The e-CSP is essentially the conventional CSP with the Euler representation. It includes the following two stages: each sample value is first mapped into a complex space by using the Euler representation, and then the conventional CSP is performed in the Euler space. Thus, the e-CSP is equivalent to applying the Euler representation as a kernel function to the input of the CSP. It is computationally as straightforward as the CSP. However, it extracts more discriminative features from the EEG signals. Extensive experimental results illustrate the discrimination ability of the e-CSP.},
}
@article {pmid35062641,
year = {2022},
author = {Kim, S and Shin, DY and Kim, T and Lee, S and Hyun, JK and Park, SM},
title = {Enhanced Recognition of Amputated Wrist and Hand Movements by Deep Learning Method Using Multimodal Fusion of Electromyography and Electroencephalography.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {2},
pages = {},
pmid = {35062641},
issn = {1424-8220},
support = {2020R1A6A1A03047902//National Research Foundation of Korea/ ; 2020R1A2C2005385, 2020R1A2C2004764//Korea government (Ministry of Science and ICT, MSIT)/ ; 202017D01//Korean government (MSIT, the Ministry of Trade, Industry and Energy, the Ministry of Health &amp; Welfare, and the Ministry of Food and Drug Safety)/ ; },
mesh = {Algorithms ; *Amputees ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Electromyography ; Humans ; Wrist ; },
abstract = {Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based motion classification techniques are limited owing to the complexity and noise of EEG signals, and the electrode placement bias, and low-resolution of EMG signals. We herein propose a novel system of two-dimensional (2D) input image feature multimodal fusion based on an EEG/EMG-signal transfer learning (TL) paradigm for detection of hand movements in transforearm amputees. A feature extraction method in the frequency domain of the EEG and EMG signals was adopted to establish a 2D image. The input images were used for training on a model based on the convolutional neural network algorithm and TL, which requires 2D images as input data. For the purpose of data acquisition, five transforearm amputees and nine healthy controls were recruited. Compared with the conventional single-modal EEG signal trained models, the proposed multimodal fusion method significantly improved classification accuracy in both the control and patient groups. When the two signals were combined and used in the pretrained model for EEG TL, the classification accuracy increased by 4.18-4.35% in the control group, and by 2.51-3.00% in the patient group.},
}
@article {pmid35062495,
year = {2022},
author = {Bagheri, M and Power, SD},
title = {Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {2},
pages = {},
pmid = {35062495},
issn = {1424-8220},
support = {RGPIN-2016-04210//Natural Sciences and Engineering Research Council/ ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Risk ; Workload ; },
abstract = {Research studies on EEG-based mental workload detection for a passive BCI generally focus on classifying cognitive states associated with the performance of tasks at different levels of difficulty, with no other aspects of the user's mental state considered. However, in real-life situations, different aspects of the user's state such as their cognitive (e.g., level of mental workload) and affective (e.g., level of stress/anxiety) states will often change simultaneously, and performance of a BCI system designed considering just one state may be unreliable. Moreover, multiple mental states may be relevant to the purposes of the BCI-for example both mental workload and stress level might be related to an aircraft pilot's risk of error-and the simultaneous prediction of states may be critical in maximizing the practical effectiveness of real-life online BCI systems. In this study we investigated the feasibility of performing simultaneous classification of mental workload and stress level in an online passive BCI. We investigated both subject-specific and cross-subject classification approaches, the latter with and without the application of a transfer learning technique to align the distributions of data from the training and test subjects. Using cross-subject classification with transfer learning in a simulated online analysis, we obtained accuracies of 77.5 ± 6.9% and 84.1 ± 5.9%, across 18 participants for mental workload and stress level detection, respectively.},
}
@article {pmid35060847,
year = {2022},
author = {Dong, L and Li, S and Lian, WH and Wei, QC and Mohamad, OAA and Hozzein, WN and Ahmed, I and Li, WJ},
title = {Sphingomonas arenae sp. nov., isolated from desert soil.},
journal = {International journal of systematic and evolutionary microbiology},
volume = {72},
number = {1},
pages = {},
doi = {10.1099/ijsem.0.005195},
pmid = {35060847},
issn = {1466-5034},
mesh = {Bacterial Typing Techniques ; Base Composition ; China ; DNA, Bacterial/genetics ; Desert Climate ; Fatty Acids/chemistry ; Phospholipids/chemistry ; *Phylogeny ; RNA, Ribosomal, 16S/genetics ; Sequence Analysis, DNA ; *Soil Microbiology ; *Sphingomonas/classification/isolation &amp; purification ; },
abstract = {Two bacterial strains, designated as SYSU D00720T and SYSU D00722, were isolated from a desert sandy soil sample collected from Gurbantunggut Desert in Xinjiang, north-west China. Cells were Gram-stain-negative, aerobic, non-motile, rod-shaped, oxidase-positive and catalase-negative. Colonies were circular, opaque, convex, smooth, orange on Reasoner's 2A (R2A) agar. The isolates were found to grow at 4-45 °C (optimum, 28-30 °C), at pH 6.0-7.0 (optimum, 7.0) and with 0-1.5 % (w/v) NaCl (optimum, 0%). Growth was observed on R2A agar, Luria-Bertani agar and nutrient agar, but not on trypticase soy agar. The polar lipids consisted of diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylglycerol, phosphatidylcholine, sphingoglycolipid, two unidentified aminolipids, one unidentified glycolipid, one unidentified aminoglycolipid, one unidentified aminophospholipid, one unidentified phospholipid and two unidentified lipids. The main fatty acids (>10%) were C17 : 1 ω6c, summed feature 8 (C18 : 1 ω7c and/or C18 : 1 ω6c) and C16 : 0. The major respiratory quinone was ubiquinone-10 and the major polyamine was sym-homospermidine. The genomic DNA G+C content was 66.0 mol%. Strains SYSU D00720T and SYSU D00722 were nearly identical with a 16S rRNA gene sequence similarity of 99.6 %, and 100.0 % average nucleotide identity (ANI), average amino acid identity (AAI) and digital DNA-DNA hybridization (dDDH) values. Phylogenetic analyses clearly demonstrated that these two strains belonged to the same species of the genus Sphingomonas, and had highest sequence similarity to Sphingomonas lutea KCTC 23642T (97.3 %). The ANI, AAI and dDDH values of strains SYSU D00720T and SYSU D00722 to S. lutea KCTC 23642T were both 73.2, 69.9 and 19.2 %, respectively. Based on phylogenetic, phenotypic and chemotaxonomic distinctiveness, strains SYSU D00720T and SYSU D00722 represent a novel species of the genus Sphingomonas, for which the name Sphingomonas arenae sp. nov. is proposed. The type strain is SYSU D00720T (=MCCC 1K05154T=NBRC 115061T).},
}
@article {pmid35059816,
year = {2022},
author = {Pugliese, R and Sala, R and Regondi, S and Beltrami, B and Lunetta, C},
title = {Emerging technologies for management of patients with amyotrophic lateral sclerosis: from telehealth to assistive robotics and neural interfaces.},
journal = {Journal of neurology},
volume = {},
number = {},
pages = {},
pmid = {35059816},
issn = {1432-1459},
abstract = {Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease, is characterized by the degeneration of both upper and lower motor neurons, which leads to muscle weakness and subsequently paralysis. It begins subtly with focal weakness but spreads relentlessly to involve most muscles, thus proving to be effectively incurable. Typically, death due to respiratory paralysis occurs in 3-5 years. To date, it has been shown that the management of ALS patients is best achieved with a multidisciplinary approach, and with the help of emerging technologies ranging from multidisciplinary teleconsults (for monitoring the dysphagia, respiratory function, and nutritional status) to brain-computer interfaces and eye tracking for alternative augmentative communication, until robotics, it may increase effectiveness. The COVID-19 pandemic created a spasmodic need to accelerate the development and implementation of such technologies in clinical practice, to improve the daily lives of both ALS patients and caregivers. However, despite the remarkable strides that have been made in the field, there are still issues to be addressed. This review will be discussed on the eureka moment of emerging technologies for ALS, used as a blueprint not only for neurodegenerative diseases, examining the current technologies already in place or being evaluated, highlighting the pros and cons for future clinical applications.},
}
@article {pmid35058858,
year = {2021},
author = {Liu, M},
title = {An EEG Neurofeedback Interactive Model for Emotional Classification of Electronic Music Compositions Considering Multi-Brain Synergistic Brain-Computer Interfaces.},
journal = {Frontiers in psychology},
volume = {12},
number = {},
pages = {799132},
doi = {10.3389/fpsyg.2021.799132},
pmid = {35058858},
issn = {1664-1078},
abstract = {This paper presents an in-depth study and analysis of the emotional classification of EEG neurofeedback interactive electronic music compositions using a multi-brain collaborative brain-computer interface (BCI). Based on previous research, this paper explores the design and performance of sound visualization in an interactive format from the perspective of visual performance design and the psychology of participating users with the help of knowledge from various disciplines such as psychology, acoustics, aesthetics, neurophysiology, and computer science. This paper proposes a specific mapping model for the conversion of sound to visual expression based on people's perception and aesthetics of sound based on the phenomenon of audiovisual association, which provides a theoretical basis for the subsequent research. Based on the mapping transformation pattern between audio and visual, this paper investigates the realization path of interactive sound visualization, the visual expression form and its formal composition, and the aesthetic style, and forms a design expression method for the visualization of interactive sound, to benefit the practice of interactive sound visualization. In response to the problem of neglecting the real-time and dynamic nature of the brain in traditional brain network research, dynamic brain networks proposed for analyzing the EEG signals induced by long-time music appreciation. During prolonged music appreciation, the connectivity of the brain changes continuously. We used mutual information on different frequency bands of EEG signals to construct dynamic brain networks, observe changes in brain networks over time and use them for emotion recognition. We used the brain network for emotion classification and achieved an emotion recognition rate of 67.3% under four classifications, exceeding the highest recognition rate available.},
}
@article {pmid35058768,
year = {2021},
author = {Jatupornpoonsub, T and Thimachai, P and Supasyndh, O and Wongsawat, Y},
title = {EEG Delta/Theta Ratio and Microstate Analysis Originating Novel Biomarkers for Malnutrition-Inflammation Complex Syndrome in ESRD Patients.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {795237},
pmid = {35058768},
issn = {1662-5161},
abstract = {The Malnutrition-Inflammation Score (MIS) was initially proposed to evaluate malnutrition-inflammation complex syndrome (MICS) in end-stage renal disease (ESRD) patients. Although MICS should be routinely evaluated to reduce the hospitalization and mortality rate of ESRD patients, the inconvenience of the MIS might limit its use. Cerebral complications in ESRD, possibly induced by MICS, were previously assessed by using spectral electroencephalography (EEG) via the delta/theta ratio and microstate analysis. Correspondingly, EEG could be used to directly assess MICS in ESRD patients, but the relationships among MICS and these EEG features remain inconclusive. Thus, we aimed to investigate the delta/theta ratio and microstates in ESRD patients with high and low risks of MICS. We also attempted to identify the correlation among the MIS, delta/theta ratio, and microstate parameters, which might clarify their relationships. To achieve these objectives, a total of forty-six ESRD subjects were willingly recruited. We collected their blood samples, MIS, and EEGs after receiving written informed consent. Sixteen women and seven men were allocated to low risk group (MIS ≤ 5, age 57.57 ± 14.88 years). Additionally, high risk group contains 15 women and 8 men (MIS > 5, age 59.13 ± 11.77 years). Here, we discovered that delta/theta ratio (p < 0.041) and most microstate parameters (p < 0.001) were significantly different between subject groups. We also found that the delta/theta ratio was not correlated with MIS but was strongly with the average microstate duration (ρ = 0.708, p < 0.001); hence, we suggested that the average microstate duration might serve as an alternative encephalopathy biomarker. Coincidentally, we noticed positive correlations for most parameters of microstates A and B (0.54 ≤ ρ ≤ 0.68, p < 0.001) and stronger negative correlations for all microstate C parameters (-0.75 ≤ ρ ≤ -0.61, p < 0.001). These findings unveiled a novel EEG biomarker, the MIC index, that could efficiently distinguish ESRD patients at high and low risk of MICS when utilized as a feature in a binary logistic regression model (accuracy of train-test split validation = 1.00). We expected that the average microstate duration and MIC index might potentially contribute to monitor ESRD patients in the future.},
}
@article {pmid35058514,
year = {2022},
author = {Kumar, N and Michmizos, KP},
title = {A neurophysiologically interpretable deep neural network predicts complex movement components from brain activity.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {1101},
pmid = {35058514},
issn = {2045-2322},
support = {K12HD093427//National Center for Medical Rehabilitation Research/ ; },
mesh = {Algorithms ; Brain/diagnostic imaging/*physiology ; Brain-Computer Interfaces ; Data Collection/methods ; Electroencephalography/methods ; Female ; Forecasting/methods ; Humans ; Male ; Movement/*physiology ; Nervous System Physiological Phenomena ; Neural Networks, Computer ; Neurological Rehabilitation/methods ; Neurophysiology/*methods ; Reaction Time ; Research Design ; Young Adult ; },
abstract = {The effective decoding of movement from non-invasive electroencephalography (EEG) is essential for informing several therapeutic interventions, from neurorehabilitation robots to neural prosthetics. Deep neural networks are most suitable for decoding real-time data but their use in EEG is hindered by the gross classes of motor tasks in the currently available datasets, which are solvable even with network architectures that do not require specialized design considerations. Moreover, the weak association with the underlying neurophysiology limits the generalizability of modern networks for EEG inference. Here, we present a neurophysiologically interpretable 3-dimensional convolutional neural network (3D-CNN) that captured the spatiotemporal dependencies in brain areas that get co-activated during movement. The 3D-CNN received topography-preserving EEG inputs, and predicted complex components of hand movements performed on a plane using a back-drivable rehabilitation robot, namely (a) the reaction time (RT) for responding to stimulus (slow or fast), (b) the mode of movement (active or passive, depending on whether there was an assistive force provided by the apparatus), and (c) the orthogonal directions of the movement (left, right, up, or down). We validated the 3D-CNN on a new dataset that we acquired from an in-house motor experiment, where it achieved average leave-one-subject-out test accuracies of 79.81%, 81.23%, and 82.00% for RT, active vs. passive, and direction classifications, respectively. Our proposed method outperformed the modern 2D-CNN architecture by a range of 1.1% to 6.74% depending on the classification task. Further, we identified the EEG sensors and time segments crucial to the classification decisions of the network, which aligned well with the current neurophysiological knowledge on brain activity in motor planning and execution tasks. Our results demonstrate the importance of biological relevance in networks for an accurate decoding of EEG, suggesting that the real-time classification of other complex brain activities may now be within our reach.},
}
@article {pmid35053846,
year = {2022},
author = {Vekety, B and Logemann, A and Takacs, ZK},
title = {Mindfulness Practice with a Brain-Sensing Device Improved Cognitive Functioning of Elementary School Children: An Exploratory Pilot Study.},
journal = {Brain sciences},
volume = {12},
number = {1},
pages = {},
pmid = {35053846},
issn = {2076-3425},
support = {LP-2018-21/2018//Eötvös Loránd Research Network/ ; K131635//Hungarian National Research, Development and Innovation Office/ ; },
abstract = {This is the first pilot study with children that has assessed the effects of a brain-computer interface-assisted mindfulness program on neural mechanisms and associated cognitive performance. The participants were 31 children aged 9-10 years who were randomly assigned to either an eight-session mindfulness training with EEG-feedback or a passive control group. Mindfulness-related brain activity was measured during the training, while cognitive tests and resting-state brain activity were measured pre- and post-test. The within-group measurement of calm/focused brain states and mind-wandering revealed a significant linear change. Significant positive changes were detected in children's inhibition, information processing, and resting-state brain activity (alpha, theta) compared to the control group. Elevated baseline alpha activity was associated with less reactivity in reaction time on a cognitive test. Our exploratory findings show some preliminary support for a potential executive function-enhancing effect of mindfulness supplemented with EEG-feedback, which may have some important implications for children's self-regulated learning and academic achievement.},
}
@article {pmid35053801,
year = {2021},
author = {Ferracuti, F and Iarlori, S and Mansour, Z and Monteriù, A and Porcaro, C},
title = {Comparing between Different Sets of Preprocessing, Classifiers, and Channels Selection Techniques to Optimise Motor Imagery Pattern Classification System from EEG Pattern Recognition.},
journal = {Brain sciences},
volume = {12},
number = {1},
pages = {},
pmid = {35053801},
issn = {2076-3425},
abstract = {The ability to control external devices through thought is increasingly becoming a reality. Human beings can use the electrical signals of their brain to interact or change the surrounding environment and more. The development of this technology called brain-computer interface (BCI) will increasingly allow people with motor disabilities to communicate or use assistive devices to walk, manipulate objects and communicate. Using data from the PhysioNet database, this study implemented a pattern classification system for use in a BCI on 109 healthy volunteers during real movement activities and motor imagery recorded by 64-channels electroencephalography (EEG) system. Different classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Trees (TREE) were applied on different combinations of EEG channels. Starting from two channels (C3, C4 and CP3 and CP4) positioned on the contralateral and ipsilateral sensorimotor cortex, the Region of Interest (RoI) centred on C3/Cp3 and C4/Cp4 and, finally, a data-driven automatic channels selection was tested to explore the best channel combination able to increase the classification accuracy. The results showed that the proposed automatic channels selection was able to significantly improve the performance of each classifier achieving 98% of accuracy for classification of real and imagined hand movement (sensitivity = 97%, specificity = 99%, AUC = 0.99) by SVM. While the accuracy of the classification between the imagery of hand and foot movements was 91% (sensitivity = 87%, specificity = 86%, AUC = 0.93) also with SVM. In the proposed approach, the data-driven automatic channels selection outperforms classical a priori channel selection models such as C3/C4, Cp3/Cp4, or RoIs around those channels with the utmost accuracy to help remove the boundaries of human communication and improve the quality of life of people with disabilities.},
}
@article {pmid35049650,
year = {2022},
author = {Altuwaijri, GA and Muhammad, G},
title = {A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification.},
journal = {Biosensors},
volume = {12},
number = {1},
pages = {},
pmid = {35049650},
issn = {2079-6374},
support = {RSP-2021/34//King Saud University, Riyadh, Saudi Arabia/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; *Neural Networks, Computer ; },
abstract = {Automatic high-level feature extraction has become a possibility with the advancement of deep learning, and it has been used to optimize efficiency. Recently, classification methods for Convolutional Neural Network (CNN)-based electroencephalography (EEG) motor imagery have been proposed, and have achieved reasonably high classification accuracy. These approaches, however, use the CNN single convolution scale, whereas the best convolution scale varies from subject to subject. This limits the precision of classification. This paper proposes multibranch CNN models to address this issue by effectively extracting the spatial and temporal features from raw EEG data, where the branches correspond to different filter kernel sizes. The proposed method's promising performance is demonstrated by experimental results on two public datasets, the BCI Competition IV 2a dataset and the High Gamma Dataset (HGD). The results of the technique show a 9.61% improvement in the classification accuracy of multibranch EEGNet (MBEEGNet) from the fixed one-branch EEGNet model, and 2.95% from the variable EEGNet model. In addition, the multibranch ShallowConvNet (MBShallowConvNet) improved the accuracy of a single-scale network by 6.84%. The proposed models outperformed other state-of-the-art EEG motor imagery classification methods.},
}
@article {pmid35049411,
year = {2022},
author = {Xie, P and Hao, S and Zhao, J and Liang, Z and Li, X},
title = {A Spatio-Temporal Method for Extracting Gamma-Band Features to Enhance Classification in a Rapid Serial Visual Presentation Task.},
journal = {International journal of neural systems},
volume = {32},
number = {3},
pages = {2250010},
doi = {10.1142/S0129065722500101},
pmid = {35049411},
issn = {1793-6462},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Discriminant Analysis ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; },
abstract = {Rapid serial visual presentation (RSVP) is a type of electroencephalogram (EEG) pattern commonly used for target recognition. Besides delta- and theta-band responses already used for classification, RSVP task also evokes gamma-band responses having low amplitude and large individual difference. This paper proposes a filter bank spatio-temporal component analysis (FBSCA) method, extracting spatio-temporal features of the gamma-band responses for the first time, to enhance the RSVP classification performance. Considering the individual difference in time latency and responsive frequency, the proposed FBSCA method decomposes the gamma-band EEG data into sub-components in different time-frequency-space domains and seeks the weight coefficients to optimize the combinations of electrodes, common spatial pattern (CSP) components, time windows and frequency bands. Two state-of-the-art methods, i.e. hierarchical discriminant principal component analysis (HDPCA) and discriminative canonical pattern matching (DCPM), were used for comparison. The performance was evaluated in [Formula: see text] cross validations using a public dataset. Study results showed that the FBSCA method outperformed the other methods regardless of number of training trials. These results suggest that the proposed FBSCA method can enhance the RSVP classification.},
}
@article {pmid35047035,
year = {2022},
author = {Ramalingam, P and Mehbodniya, A and Webber, JL and Shabaz, M and Gopalakrishnan, L},
title = {Telemetry Data Compression Algorithm Using Balanced Recurrent Neural Network and Deep Learning.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {4886586},
pmid = {35047035},
issn = {1687-5273},
mesh = {Algorithms ; *Data Compression ; *Deep Learning ; Neural Networks, Computer ; Telemetry ; },
abstract = {Telemetric information is great in size, requiring extra room and transmission time. There is a significant obstruction of storing or sending telemetric information. Lossless data compression (LDC) algorithms have evolved to process telemetric data effectively and efficiently with a high compression ratio and a short processing time. Telemetric information can be packed to control the extra room and association data transmission. In spite of the fact that different examinations on the pressure of telemetric information have been conducted, the idea of telemetric information makes pressure incredibly troublesome. The purpose of this study is to offer a subsampled and balanced recurrent neural lossless data compression (SB-RNLDC) approach for increasing the compression rate while decreasing the compression time. This is accomplished through the development of two models: one for subsampled averaged telemetry data preprocessing and another for BRN-LDC. Subsampling and averaging are conducted at the preprocessing stage using an adjustable sampling factor. A balanced compression interval (BCI) is used to encode the data depending on the probability measurement during the LDC stage. The aim of this research work is to compare differential compression techniques directly. The final output demonstrates that the balancing-based LDC can reduce compression time and finally improve dependability. The final experimental results show that the model proposed can enhance the computing capabilities in data compression compared to the existing methodologies.},
}
@article {pmid35046522,
year = {2022},
author = {Serino, A and Bockbrader, M and Bertoni, T and Colachis Iv, S and Solcà, M and Dunlap, C and Eipel, K and Ganzer, P and Annetta, N and Sharma, G and Orepic, P and Friedenberg, D and Sederberg, P and Faivre, N and Rezai, A and Blanke, O},
title = {Sense of agency for intracortical brain-machine interfaces.},
journal = {Nature human behaviour},
volume = {6},
number = {4},
pages = {565-578},
pmid = {35046522},
issn = {2397-3374},
support = {PP00P3_163951//Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation)/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; Movement ; },
abstract = {Intracortical brain-machine interfaces decode motor commands from neural signals and translate them into actions, enabling movement for paralysed individuals. The subjective sense of agency associated with actions generated via intracortical brain-machine interfaces, the neural mechanisms involved and its clinical relevance are currently unknown. By experimentally manipulating the coherence between decoded motor commands and sensory feedback in a tetraplegic individual using a brain-machine interface, we provide evidence that primary motor cortex processes sensory feedback, sensorimotor conflicts and subjective states of actions generated via the brain-machine interface. Neural signals processing the sense of agency affected the proficiency of the brain-machine interface, underlining the clinical potential of the present approach. These findings show that primary motor cortex encodes information related to action and sensing, but also sensorimotor and subjective agency signals, which in turn are relevant for clinical applications of brain-machine interfaces.},
}
@article {pmid35045797,
year = {2022},
author = {Hinvest, NS and Ashwin, C and Carter, F and Hook, J and Smith, LGE and Stothart, G},
title = {An Empirical Evaluation of Methodologies Used for Emotion Recognition via EEG Signals.},
journal = {Social neuroscience},
volume = {17},
number = {1},
pages = {1-12},
doi = {10.1080/17470919.2022.2029558},
pmid = {35045797},
issn = {1747-0927},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Emotions ; Humans ; },
abstract = {A goal of brain-computer-interface (BCI) research is to accurately classify participants' emotional status via objective measurements. While there has been a growth in EEG-BCI literature tackling this issue, there exist methodological limitations that undermine its ability to reach conclusions. These include both the nature of the stimuli used to induce emotions and the steps used to process and analyze the data. To highlight and overcome these limitations we appraised whether previous literature using commonly used, widely available, datasets is purportedly classifying between emotions based on emotion-related signals of interest and/or non-emotional artifacts. Subsequently, we propose new methods based on empirically driven, scientifically rigorous, foundations. We close by providing guidance to any researcher involved or wanting to work within this dynamic research field.},
}
@article {pmid35044123,
year = {2021},
author = {Borisova, VA and Isakova, EV and Kotov, SV},
title = {[Cognitive rehabilitation after stroke using non-pharmacological approaches].},
journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova},
volume = {121},
number = {12. Vyp. 2},
pages = {26-32},
doi = {10.17116/jnevro202112112226},
pmid = {35044123},
issn = {1997-7298},
mesh = {Brain ; Cognition ; Humans ; *Stroke/therapy ; *Stroke Rehabilitation ; Transcranial Magnetic Stimulation ; },
abstract = {Cerebral stroke is one of the leading causes of disability in the modern world. Despite the high efficacy of high-tech treatment methods, the issue of rehabilitation is extremely relevant for post-stroke patients. Much attention is paid to non-pharmacological approaches; their use in the treatment process seems to be very promising. The review presents therapeutic approaches aimed at increasing the plasticity of the brain, including rhythmic transcranial magnetic stimulation, direct current stimulation, training with a speech therapist-neuropsychologist, computerized cognitive training, biofeedback techniques for support response, electroencephalogram, high-tech approaches using virtual reality, interfaces brain-computer, art therapy, music therapy, various complexes of physical exercises.},
}
@article {pmid35043274,
year = {2022},
author = {Colamarino, E and Pichiorri, F and Toppi, J and Mattia, D and Cincotti, F},
title = {Automatic Selection of Control Features for Electroencephalography-Based Brain-Computer Interface Assisted Motor Rehabilitation: The GUIDER Algorithm.},
journal = {Brain topography},
volume = {35},
number = {2},
pages = {182-190},
pmid = {35043274},
issn = {1573-6792},
support = {RF-2018-12365210//Ministero della Salute/ ; RF-2019-12369396//Ministero della Salute/ ; GR-2018-12365874//Ministero della Salute/ ; RM120172B8899B8C//Sapienza Università di Roma/ ; },
abstract = {Sensorimotor rhythms-based Brain-Computer Interfaces (BCIs) have successfully been employed to address upper limb motor rehabilitation after stroke. In this context, becomes crucial the choice of features that would enable an appropriate electroencephalographic (EEG) sensorimotor activation/engagement underlying the favourable motor recovery. Here, we present a novel feature selection algorithm (GUIDER) designed and implemented to integrate specific requirements related to neurophysiological knowledge and rehabilitative principles. The GUIDER algorithm was tested on an EEG dataset collected from 13 subacute stroke participants. The comparison between the automatic feature selection procedure by means of GUIDER algorithm and the manual feature selection executed by an expert neurophysiologist returned similar performance in terms of both feature selection and classification. Our preliminary findings suggest that the choices of experienced neurophysiologists could be reproducible by an automatic approach. The proposed automatic algorithm could be apt to support the professional end-users not expert in BCI such as therapist/clinicians and, to ultimately foster a wider employment of the BCI-based rehabilitation after stroke.},
}
@article {pmid35041605,
year = {2022},
author = {Lee, DY and Jeong, JH and Lee, BH and Lee, SW},
title = {Motor Imagery Classification Using Inter-Task Transfer Learning via a Channel-Wise Variational Autoencoder-Based Convolutional Neural Network.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {226-237},
doi = {10.1109/TNSRE.2022.3143836},
pmid = {35041605},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Hand ; Humans ; Imagination ; Machine Learning ; Neural Networks, Computer ; },
abstract = {Highly sophisticated control based on a brain-computer interface (BCI) requires decoding kinematic information from brain signals. The forearm is a region of the upper limb that is often used in everyday life, but intuitive movements within the same limb have rarely been investigated in previous BCI studies. In this study, we focused on various forearm movement decoding from electroencephalography (EEG) signals using a small number of samples. Ten healthy participants took part in an experiment and performed motor execution (ME) and motor imagery (MI) of the intuitive movement tasks (Dataset I). We propose a convolutional neural network using a channel-wise variational autoencoder (CVNet) based on inter-task transfer learning. We approached that training the reconstructed ME-EEG signals together will also achieve more sufficient classification performance with only a small amount of MI-EEG signals. The proposed CVNet was validated on our own Dataset I and a public dataset, BNCI Horizon 2020 (Dataset II). The classification accuracies of various movements are confirmed to be 0.83 (±0.04) and 0.69 (±0.04) for Dataset I and II, respectively. The results show that the proposed method exhibits performance increases of approximately 0.09~0.27 and 0.08~0.24 compared with the conventional models for Dataset I and II, respectively. The outcomes suggest that the training model for decoding imagined movements can be performed using data from ME and a small number of data samples from MI. Hence, it is presented the feasibility of BCI learning strategies that can sufficiently learn deep learning with a few amount of calibration dataset and time only, with stable performance.},
}
@article {pmid35038681,
year = {2022},
author = {Ancau, DM and Ancau, M and Ancau, M},
title = {Deep-learning online EEG decoding brain-computer interface using error-related potentials recorded with a consumer-grade headset.},
journal = {Biomedical physics &amp; engineering express},
volume = {8},
number = {2},
pages = {},
doi = {10.1088/2057-1976/ac4c28},
pmid = {35038681},
issn = {2057-1976},
mesh = {Algorithms ; Artificial Intelligence ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Humans ; },
abstract = {Objective.Brain-computer interfaces (BCIs) allow subjects with sensorimotor disability to interact with the environment. Non-invasive BCIs relying on EEG signals such as event-related potentials (ERPs) have been established as a reliable compromise between spatio-temporal resolution and patient impact, but limitations due to portability and versatility preclude their broad application. Here we describe a deep-learning augmented error-related potential (ErrP) discriminating BCI using a consumer-grade portable headset EEG, the Emotiv EPOC+.Approach.We recorded and discriminated ErrPs offline and online from 14 subjects during a visual feedback task.Main results:We achieved online discrimination accuracies of up to 81%, comparable to those obtained with professional 32/64-channel EEG devices via deep-learning using either a generative-adversarial network or an intrinsic-mode function augmentation of the training data and minimalistic computing resources.Significance.Our BCI model has the potential of expanding the spectrum of BCIs to more portable, artificial intelligence-enhanced, efficient interfaces accelerating the routine deployment of these devices outside the controlled environment of a scientific laboratory.},
}
@article {pmid35036688,
year = {2022},
author = {Li, B and Sun, H and Shu, H and Wang, X},
title = {Applying Neuromorphic Computing Simulation in Band Gap Prediction and Chemical Reaction Classification.},
journal = {ACS omega},
volume = {7},
number = {1},
pages = {168-175},
pmid = {35036688},
issn = {2470-1343},
abstract = {The rapidly developing artificial intelligence (AI) requires revolutionary computing architectures to break the energy efficiency bottleneck caused by the traditional von Neumann computing architecture. In addition, the emerging brain-machine interface also requires computational circuitry that can conduct large parallel computational tasks with low energy cost and good biocompatibility. Neuromorphic computing, a novel computational architecture emulating human brains, has drawn significant interest for the aforementioned applications due to its low energy cost, capability to parallelly process large-scale data, and biocompatibility. Most efforts in the domain of neuromorphic computing focus on addressing traditional AI problems, such as handwritten digit recognition and file classification. Here, we demonstrate for the first time that current neuromorphic computing techniques can be used to solve key machine learning questions in cheminformatics. We predict the band gaps of small-molecule organic semiconductors and classify chemical reaction types with a simulated neuromorphic circuitry. Our work can potentially guide the design and fabrication of elementary devices and circuitry for neuromorphic computing specialized for chemical purposes.},
}
@article {pmid35036481,
year = {2022},
author = {Ghosh, R and Deb, N and Sengupta, K and Phukan, A and Choudhury, N and Kashyap, S and Phadikar, S and Saha, R and Das, P and Sinha, N and Dutta, P},
title = {SAM 40: Dataset of 40 subject EEG recordings to monitor the induced-stress while performing Stroop color-word test, arithmetic task, and mirror image recognition task.},
journal = {Data in brief},
volume = {40},
number = {},
pages = {107772},
pmid = {35036481},
issn = {2352-3409},
abstract = {This paper presents a collection of electroencephalogram (EEG) data recorded from 40 subjects (female: 14, male: 26, mean age: 21.5 years). The dataset was recorded from the subjects while performing various tasks such as Stroop color-word test, solving arithmetic questions, identification of symmetric mirror images, and a state of relaxation. The experiment was primarily conducted to monitor the short-term stress elicited in an individual while performing the aforementioned cognitive tasks. The individual tasks were carried out for 25 s and were repeated to record three trials. The EEG was recorded using a 32-channel Emotiv Epoc Flex gel kit. The EEG data were then segmented into non-overlapping epochs of 25 s depending on the various tasks performed by the subjects. The EEG data were further processed to remove the baseline drifts by subtracting the average trend obtained using the Savitzky-Golay filter. Furthermore, the artifacts were also removed from the EEG data by applying wavelet thresholding. The dataset proposed in this paper can aid and support the research activities in the field of brain-computer interface and can also be used in the identification of patterns in the EEG data elicited due to stress.},
}
@article {pmid35034741,
year = {2022},
author = {Pozzi, NG and Isaias, IU},
title = {Adaptive deep brain stimulation: Retuning Parkinson's disease.},
journal = {Handbook of clinical neurology},
volume = {184},
number = {},
pages = {273-284},
doi = {10.1016/B978-0-12-819410-2.00015-1},
pmid = {35034741},
issn = {0072-9752},
mesh = {*Brain-Computer Interfaces ; *Deep Brain Stimulation ; Humans ; *Parkinson Disease/therapy ; },
abstract = {A brain-machine interface represents a promising therapeutic avenue for the treatment of many neurologic conditions. Deep brain stimulation (DBS) is an invasive, neuro-modulatory tool that can improve different neurologic disorders by delivering electric stimulation to selected brain areas. DBS is particularly successful in advanced Parkinson's disease (PD), where it allows sustained improvement of motor symptoms. However, this approach is still poorly standardized, with variable clinical outcomes. To achieve an optimal therapeutic effect, novel adaptive DBS (aDBS) systems are being developed. These devices operate by adapting stimulation parameters in response to an input signal that can represent symptoms, motor activity, or other behavioral features. Emerging evidence suggests greater efficacy with fewer adverse effects during aDBS compared with conventional DBS. We address this topic by discussing the basics principles of aDBS, reviewing current evidence, and tackling the many challenges posed by aDBS for PD.},
}
@article {pmid35030477,
year = {2022},
author = {Santamaría-Vázquez, E and Martínez-Cagigal, V and Pérez-Velasco, S and Marcos-Martínez, D and Hornero, R},
title = {Robust asynchronous control of ERP-Based brain-Computer interfaces using deep learning.},
journal = {Computer methods and programs in biomedicine},
volume = {215},
number = {},
pages = {106623},
doi = {10.1016/j.cmpb.2022.106623},
pmid = {35030477},
issn = {1872-7565},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Evoked Potentials ; Humans ; Neural Networks, Computer ; },
abstract = {BACKGROUND AND OBJECTIVE: Brain-computer interfaces (BCI) based on event-related potentials (ERP) are a promising technology for alternative and augmented communication in an assistive context. However, most approaches to date are synchronous, requiring the intervention of a supervisor when the user wishes to turn his attention away from the BCI system. In order to bring these BCIs into real-life applications, a robust asynchronous control of the system is required through monitoring of user attention. Despite the great importance of this limitation, which prevents the deployment of these systems outside the laboratory, it is often overlooked in research articles. This study was aimed to propose a novel method to solve this problem, taking advantage of deep learning for the first time in this context to overcome the limitations of previous strategies based on hand-crafted features.<br><br>METHODS: The proposed method, based on EEG-Inception, a novel deep convolutional neural network, divides the problem in 2 stages to achieve the asynchronous control: (i) the model detects user's control state, and (ii) decodes the command only if the user is attending to the stimuli. Additionally, we used transfer learning to reduce the calibration time, even exploring a calibration-less approach.<br><br>RESULTS: Our method was evaluated with 22 healthy subjects, analyzing the impact of the calibration time and number of stimulation sequences on the system's performance. For the control state detection stage, we report average accuracies above 91% using only 1 sequence of stimulation and 30 calibration trials, reaching a maximum of 96.95% with 15 sequences. Moreover, our calibration-less approach also achieved suitable results, with a maximum accuracy of 89.36%, showing the benefits of transfer learning. As for the overall asynchronous system, which includes both stages, the maximum information transfer rate was 35.54 bpm, a suitable value for high-speed communication.<br><br>CONCLUSIONS: The proposed strategy achieved higher performance with less calibration trials and stimulation sequences than former approaches, representing a promising step forward that paves the way for more practical applications of ERP-based spellers.},
}
@article {pmid35030232,
year = {2022},
author = {Simar, C and Petit, R and Bozga, N and Leroy, A and Cebolla, AM and Petieau, M and Bontempi, G and Cheron, G},
title = {Riemannian classification of single-trial surface EEG and sources during checkerboard and navigational images in humans.},
journal = {PloS one},
volume = {17},
number = {1},
pages = {e0262417},
pmid = {35030232},
issn = {1932-6203},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Healthy Volunteers ; Humans ; Image Processing, Computer-Assisted/*methods ; Male ; Signal Processing, Computer-Assisted ; Visual Perception/physiology ; },
abstract = {OBJECTIVE: Different visual stimuli are classically used for triggering visual evoked potentials comprising well-defined components linked to the content of the displayed image. These evoked components result from the average of ongoing EEG signals in which additive and oscillatory mechanisms contribute to the component morphology. The evoked related potentials often resulted from a mixed situation (power variation and phase-locking) making basic and clinical interpretations difficult. Besides, the grand average methodology produced artificial constructs that do not reflect individual peculiarities. This motivated new approaches based on single-trial analysis as recently used in the brain-computer interface field.<br><br>APPROACH: We hypothesize that EEG signals may include specific information about the visual features of the displayed image and that such distinctive traits can be identified by state-of-the-art classification algorithms based on Riemannian geometry. The same classification algorithms are also applied to the dipole sources estimated by sLORETA.<br><br>MAIN RESULTS AND SIGNIFICANCE: We show that our classification pipeline can effectively discriminate between the display of different visual items (Checkerboard versus 3D navigational image) in single EEG trials throughout multiple subjects. The present methodology reaches a single-trial classification accuracy of about 84% and 93% for inter-subject and intra-subject classification respectively using surface EEG. Interestingly, we note that the classification algorithms trained on sLORETA sources estimation fail to generalize among multiple subjects (63%), which may be due to either the average head model used by sLORETA or the subsequent spatial filtering failing to extract discriminative information, but reach an intra-subject classification accuracy of 82%.},
}
@article {pmid35026688,
year = {2022},
author = {Stirner, M and Gurevitch, G and Lubianiker, N and Hendler, T and Schmahl, C and Paret, C},
title = {An Investigation of Awareness and Metacognition in Neurofeedback with the Amygdala Electrical Fingerprint.},
journal = {Consciousness and cognition},
volume = {98},
number = {},
pages = {103264},
doi = {10.1016/j.concog.2021.103264},
pmid = {35026688},
issn = {1090-2376},
mesh = {Amygdala/physiology ; Brain Mapping ; Electroencephalography ; Humans ; Magnetic Resonance Imaging ; *Metacognition ; *Neurofeedback/physiology ; },
abstract = {Awareness theory posits that individuals connected to a brain-computer interface can learn to estimate and discriminate their brain states. We used the amygdala Electrical Fingerprint (amyg-EFP) - a functional Magnetic Resonance Imaging-inspired Electroencephalogram surrogate of deep brain activation - to investigate whether participants could accurately estimate their own brain activation. Ten participants completed up to 20 neurofeedback runs and estimated their amygdala-EFP activation (depicted as a thermometer) and confidence in this rating during each trial. We analysed data using multilevel models, predicting the real thermometer position with participant rated position and adjusted for activation during the previous trial. Hypotheses on learning regulation and improvement of estimation were not confirmed. However, participant ratings were significantly associated with the amyg-EFP signal. Higher rating accuracy also predicted higher subjective confidence in the rating. This proof-of-concept study introduces an approach to study awareness with fMRI-informed neurofeedback and provides initial evidence for metacognition in neurofeedback.},
}
@article {pmid35026453,
year = {2022},
author = {Ogiwara, T and Fujii, Y and Hanaoka, Y and Kitamura, S and Kuwabara, H and Funato, K and Inomata, Y and Yamazaki, D and Yamazaki, K and Murase, H and Yokota, A and Hardian, RF and Goto, T and Hongo, K and Horiuchi, T},
title = {Intraoperative Image-Guided Surgery for Gliomas in the Smart Cyber Operating Theater (SCOT): A Preliminary Clinical Application.},
journal = {World neurosurgery},
volume = {160},
number = {},
pages = {e314-e321},
doi = {10.1016/j.wneu.2022.01.012},
pmid = {35026453},
issn = {1878-8769},
mesh = {*Brain Neoplasms/pathology/surgery ; *Glioma/pathology/surgery ; Humans ; Magnetic Resonance Imaging ; Middle Aged ; Neurosurgical Procedures/methods ; Operating Rooms ; Retrospective Studies ; *Surgery, Computer-Assisted ; },
abstract = {BACKGROUND: Various devices exist for glioma image-guided surgery to improve tumor resection. These devices work as stand-alone units, making the flow of operative information complicated and disjointed. A novel networked operating room, the Smart Cyber Operating Theater (SCOT), has been developed, integrating stand-alone medical devices using the OPeLiNK communication interface. We report and evaluate the impact of SCOT for glioma surgery and our initial experiences.<br><br>METHODS: Patients with gliomas who underwent tumor resection in SCOT between July 2018 and June 2021 were retrospectively reviewed. Various types of intraoperative information were integrated, managed, and shared with the surgical strategy desk using OPeLiNK. Patients' demographics, tumor characteristics, treatment details, and outcomes were obtained. The impact of the SCOT system was evaluated.<br><br>RESULTS: Twenty-seven patients, with a mean age of 48.6 years (range, 13-88 years), met the inclusion criteria. We successfully completed all the surgical procedures using SCOT. The mean operation time was 420.6 minutes (range, 225-667 minutes).Gross total resection was accomplished in 13 patients (48.1%), subtotal resection in 4 (14.8%), and partial resection in 10 (37.0%). The main surgeon in the operating room and other neurosurgeons at the strategy desk shared and discussed the information in real time during the procedures.<br><br>CONCLUSIONS: The use of SCOT was shown to be safe and feasible in glioma surgery. This study suggests that SCOT may improve surgical outcomes and educational impact by sharing information in real time with the strategy desk.},
}
@article {pmid35026286,
year = {2022},
author = {Yi, GL and Zhu, MZ and Cui, HC and Yuan, XR and Liu, P and Tang, J and Li, YQ and Zhu, XH},
title = {A hippocampus dependent neural circuit loop underlying the generation of auditory mismatch negativity.},
journal = {Neuropharmacology},
volume = {206},
number = {},
pages = {108947},
doi = {10.1016/j.neuropharm.2022.108947},
pmid = {35026286},
issn = {1873-7064},
mesh = {Animals ; Auditory Cortex/drug effects/*physiology ; Auditory Perception/drug effects/*physiology ; CA1 Region, Hippocampal/drug effects/physiology ; Discrimination, Psychological/drug effects/physiology ; Entorhinal Cortex/drug effects/*physiology ; Evoked Potentials, Auditory/drug effects/*physiology ; Excitatory Amino Acid Antagonists/*pharmacology ; Fear/physiology ; Hippocampus/drug effects/*physiology ; Ketamine/*pharmacology ; Mice ; Nerve Net/drug effects/*physiology ; },
abstract = {Extracting relevant information and transforming it into appropriate behavior, is a fundamental brain function, and requires the coordination between the sensory and cognitive systems, however, the underlying mechanisms of interplay between sensory and cognition systems remain largely unknown. Here, we developed a mouse model for mimicking human auditory mismatch negativity (MMN), a well-characterized translational biomarker for schizophrenia, and an index of early auditory information processing. We found that a subanesthetic dose of ketamine decreased the amplitude of MMN in adult mice. Using pharmacological and chemogenetic approaches, we identified an auditory cortex-entorhinal cortex-hippocampus neural circuit loop that is required for the generation of MMN. In addition, we found that inhibition of dCA1→MEC circuit impaired the auditory related fear discrimination. Moreover, we found that ketamine induced MMN deficiency by inhibition of long-range GABAergic projection from the CA1 region of the dorsal hippocampus to the medial entorhinal cortex. These results provided circuit insights for ketamine effects and early auditory information processing. As the entorhinal cortex is the interface between the neocortex and hippocampus, and the hippocampus is critical for the formation, consolidation, and retrieval of episodic memories and other cognition, our results provide a neural mechanism for the interplay between the sensory and cognition systems.},
}
@article {pmid35025745,
year = {2022},
author = {Yan, W and Xu, G and Du, Y and Chen, X},
title = {SSVEP-EEG Feature Enhancement Method Using an Image Sharpening Filter.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {115-123},
doi = {10.1109/TNSRE.2022.3142736},
pmid = {35025745},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {Steady-state visual evoked potential (SSVEP) is widely used in brain computer interface (BCI), medical detection, and neuroscience, so there is significant interest in enhancing SSVEP features via signal processing for better performance. In this study, an image processing method was combined with brain signal analysis and a sharpening filter was used to extract image details and features for the enhancement of SSVEP features. The results demonstrated that sharpening filter could eliminate the SSVEP signal trend term and suppress its low-frequency component. Meanwhile, sharpening filter effectively enhanced the signal-to-noise ratios (SNRs) of the single-channel and multi-channel fused signals. Image sharpening filter also significantly improved the recognition accuracy of canonical correlation analysis (CCA), filter bank canonical correlation analysis (FBCCA), and task-related component analysis (TRCA). The tools developed here effectively enhanced the SSVEP signal features, suggesting that image processing methods can be considered for improved brain signal analysis.},
}
@article {pmid35023812,
year = {2022},
author = {Cai, Z and Wang, L and Guo, M and Xu, G and Guo, L and Li, Y},
title = {From Intricacy to Conciseness: A Progressive Transfer Strategy for EEG-Based Cross-Subject Emotion Recognition.},
journal = {International journal of neural systems},
volume = {32},
number = {3},
pages = {2250005},
doi = {10.1142/S0129065722500058},
pmid = {35023812},
issn = {1793-6462},
mesh = {*Algorithms ; Databases, Factual ; *Electroencephalography/methods ; Emotions ; Humans ; Learning ; },
abstract = {Emotion plays a significant role in human daily activities, and it can be effectively recognized from EEG signals. However, individual variability limits the generalization of emotion classifiers across subjects. Domain adaptation (DA) is a reliable method to solve the issue. Due to the nonstationarity of EEG, the inferior-quality source domain data bring negative transfer in DA procedures. To solve this problem, an auto-augmentation joint distribution adaptation (AA-JDA) method and a burden-lightened and source-preferred JDA (BLSP-JDA) approach are proposed in this paper. The methods are based on a novel transfer idea, learning the specific knowledge of the target domain from the samples that are appropriate for transfer, which reduces the difficulty of transfer between two domains. On multiple emotion databases, our model shows state-of-the-art performance.},
}
@article {pmid35016752,
year = {2021},
author = {Riglietti, G and Avatefipour, A and Trucco, P},
title = {The impact of business continuity management on the components of supply chain resilience: A quantitative analysis.},
journal = {Journal of business continuity &amp; emergency planning},
volume = {15},
number = {2},
pages = {182-195},
pmid = {35016752},
issn = {1749-9216},
mesh = {*Disaster Planning ; },
abstract = {This study investigates the mitigating influence of business continuity management (BCM) with respect to supply chain disruptions. Using a dataset from the 2017 BCI Supply Chain Resilience Report, the authors conduct partial least square-based structural equation modelling with reflective constructs for both exogenous and endogenous variables. The results demonstrate that BCM reduces vulnerability and mitigates the impact of supply chain disruptions on operational performance. The study highlights BCM's contribution to such important components of supply chain resilience as visibility, collaboration and agility. In addition to demonstrating the impact of BCM on supply chain resilience, the paper explains the role of top management in the BCM process, and provides a list of measures that organisations can take to protect themselves from external threats. This is the first study to use statistical analysis to provide empirical validation in this field, while employing a clear definition of BCM in line with international best practices.},
}
@article {pmid35016160,
year = {2022},
author = {Zhang, S and Chen, X and Wang, Y and Liu, B and Gao, X},
title = {Visual field inhomogeneous in brain-computer interfaces based on rapid serial visual presentation.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac4a3e},
pmid = {35016160},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Photic Stimulation/methods ; Visual Fields ; },
abstract = {Objective. Visual attention is not homogeneous across the visual field, while how to mine the effective electroencephalogram (EEG) characteristics that are sensitive to the inhomogeneous of visual attention and further explore applications such as the performance of brain-computer interface (BCI) are still distressing explorative scientists.Approach. Images were encoded into a rapid serial visual presentation (RSVP) paradigm, and were presented in three visuospatial patterns (central, left/right, upper/lower) at the stimulation frequencies of 10, 15 and 20 Hz. The comparisons among different visual fields were conducted in the dimensions of subjective behavioral and EEG characteristics. Furthermore, the effective features (e.g. steady-state visual evoked potential (SSVEP), N2-posterior-contralateral (N2pc) and P300) that sensitive to visual-field asymmetry were also explored.Main results. The visual fields had significant influences on the performance of RSVP target detection, in which the performance of central was better than that of peripheral visual field, the performance of horizontal meridian was better than that of vertical meridian, the performance of left visual field was better than that of right visual field, and the performance of upper visual field was better than that of lower visual field. Furthermore, stimuli of different visual fields had significant effects on the spatial distributions of EEG, in which N2pc and P300 showed left-right asymmetry in occipital and frontal regions, respectively. In addition, the evidences of SSVEP characteristics indicated that there was obvious overlap of visual fields on the horizontal meridian, but not on the vertical meridian.Significance. The conclusions of this study provide insights into the relationship between visual field inhomogeneous and EEG characteristics. In addition, this study has the potential to achieve precise positioning of the target's spatial orientation in RSVP-BCIs.},
}
@article {pmid35014529,
year = {2021},
author = {He, C and Ke, M and Zhong, Z and Ye, Q and He, L and Chen, Y and Zhou, J},
title = {Effect of the Degree of Acetylation of Chitin Nonwoven Fabrics for Promoting Wound Healing.},
journal = {ACS applied bio materials},
volume = {4},
number = {2},
pages = {1833-1842},
doi = {10.1021/acsabm.0c01536},
pmid = {35014529},
issn = {2576-6422},
mesh = {Acetylation ; Animals ; Biocompatible Materials/chemical synthesis/chemistry/*pharmacology ; Chitin/chemical synthesis/chemistry/*pharmacology ; Female ; Materials Testing ; Particle Size ; Rats ; Rats, Sprague-Dawley ; Skin/*drug effects/pathology ; *Textiles ; Wound Healing/*drug effects ; },
abstract = {Chitin and chitosan have been extensively used as wound dressings because of their special functions to promote wound healing. However, there was little focus on the effects of the degree of acetylation (DA) on wound healing. In this work, the regenerated chitin nonwoven fabrics with DA values of 90, 71, 60, and 42% were prepared, and the morphology and physical performances of the fabrics were characterized. Moreover, the effects of DA of the chitin nonwoven fabrics on wound recovery were studied with a full-thickness skin defect model in rats. In vitro experiments indicated that the chitin nonwoven fabrics exhibited good biocompatibility and blood compatibility and a low blood-clotting index (BCI). In vivo experiments revealed that the chitin nonwoven fabrics could accelerate wound healing more effectively than gauze by promoting re-epithelialization and collagen deposition as well as by stimulating neovascularization. The results of the wound healing process showed that DA of the chitin nonwoven fabrics had a profound effect on promoting wound healing. Notably, the regenerated chitin nonwoven fabrics with 71% DA significantly improved the wound healing compared to the commercial wound dressing Algoplaque film. Therefore, the regenerated chitin nonwoven fabrics are promising candidates for wound healing.},
}
@article {pmid35013268,
year = {2022},
author = {Proix, T and Delgado Saa, J and Christen, A and Martin, S and Pasley, BN and Knight, RT and Tian, X and Poeppel, D and Doyle, WK and Devinsky, O and Arnal, LH and Mégevand, P and Giraud, AL},
title = {Imagined speech can be decoded from low- and cross-frequency intracranial EEG features.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {48},
pmid = {35013268},
issn = {2041-1723},
mesh = {Adult ; Brain/diagnostic imaging ; Brain Mapping ; *Brain-Computer Interfaces ; *Electrocorticography ; Electrodes ; Female ; Humans ; Imagination ; *Language ; Male ; Middle Aged ; Phonetics ; *Speech ; Young Adult ; },
abstract = {Reconstructing intended speech from neural activity using brain-computer interfaces holds great promises for people with severe speech production deficits. While decoding overt speech has progressed, decoding imagined speech has met limited success, mainly because the associated neural signals are weak and variable compared to overt speech, hence difficult to decode by learning algorithms. We obtained three electrocorticography datasets from 13 patients, with electrodes implanted for epilepsy evaluation, who performed overt and imagined speech production tasks. Based on recent theories of speech neural processing, we extracted consistent and specific neural features usable for future brain computer interfaces, and assessed their performance to discriminate speech items in articulatory, phonetic, and vocalic representation spaces. While high-frequency activity provided the best signal for overt speech, both low- and higher-frequency power and local cross-frequency contributed to imagined speech decoding, in particular in phonetic and vocalic, i.e. perceptual, spaces. These findings show that low-frequency power and cross-frequency dynamics contain key information for imagined speech decoding.},
}
@article {pmid35010288,
year = {2021},
author = {Abuín-Porras, V and Martinez-Perez, C and Romero-Morales, C and Cano-de-la-Cuerda, R and Martín-Casas, P and Palomo-López, P and Sánchez-Tena, MÁ},
title = {Citation Network Study on the Use of New Technologies in Neurorehabilitation.},
journal = {International journal of environmental research and public health},
volume = {19},
number = {1},
pages = {},
pmid = {35010288},
issn = {1660-4601},
mesh = {Bibliometrics ; *Brain-Computer Interfaces ; Databases, Factual ; Humans ; *Neurological Rehabilitation ; *Neurosciences ; },
abstract = {New technologies in neurorehabilitation is a wide concept that intends to find solutions for individual and collective needs through technical systems. Analysis through citation networks is used to search scientific literature related to a specific topic. On the one hand, the main countries, institutions, and authors researching this topic have been identified, as well as their evolution over time. On the other hand, the links between the authors, the countries, and the topics under research have been analyzed. The publications analysis was performed through the Web of Science database using the search terms "new technolog*," "neurorehabilitation," "physical therapy*," and "occupational therapy*." The selected interval of publication was from 1992 to December 2020. The results were analyzed using CitNetExplorer software. After a Web of Science search, a total of 454 publications and 135 citation networks were found, 1992 being the first year of publication. An exponential increase was detected from the year 2009. The largest number was detected in 2020. The main areas are rehabilitation and neurosciences and neurology. The most cited article was from Perry et al. in 2007, with a citation index of 460. The analysis of the top 20 most cited articles shows that most approach the use of robotic devices and brain-computer interface systems. In conclusion, the main theme was found to be the use of robotic devices to address neuromuscular rehabilitation goals and brain-computer interfaces and their applications in neurorehabilitation.},
}
@article {pmid35009860,
year = {2022},
author = {Palumbo, A and Ielpo, N and Calabrese, B},
title = {An FPGA-Embedded Brain-Computer Interface System to Support Individual Autonomy in Locked-In Individuals.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {1},
pages = {},
pmid = {35009860},
issn = {1424-8220},
mesh = {Brain ; *Brain-Computer Interfaces ; Computers ; Electroencephalography ; Event-Related Potentials, P300 ; Humans ; Reproducibility of Results ; User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCI) can detect specific EEG patterns and translate them into control signals for external devices by providing people suffering from severe motor disabilities with an alternative/additional channel to communicate and interact with the outer world. Many EEG-based BCIs rely on the P300 event-related potentials, mainly because they require training times for the user relatively short and provide higher selection speed. This paper proposes a P300-based portable embedded BCI system realized through an embedded hardware platform based on FPGA (field-programmable gate array), ensuring flexibility, reliability, and high-performance features. The system acquires EEG data during user visual stimulation and processes them in a real-time way to correctly detect and recognize the EEG features. The BCI system is designed to allow to user to perform communication and domotic controls.},
}
@article {pmid35009639,
year = {2021},
author = {McDermott, EJ and Raggam, P and Kirsch, S and Belardinelli, P and Ziemann, U and Zrenner, C},
title = {Artifacts in EEG-Based BCI Therapies: Friend or Foe?.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {1},
pages = {},
pmid = {35009639},
issn = {1424-8220},
support = {13GW0213A//Federal Ministry of Education and Research/ ; },
mesh = {Algorithms ; Artifacts ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Hand ; Humans ; *Neurofeedback ; Signal Processing, Computer-Assisted ; },
abstract = {EEG-based brain-computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with specific brain states. While BCI algorithms are often designed to focus on whichever portion of a signal is most informative, in these brain-state-synchronized applications, it is of critical importance that the resulting decoder is sensitive to physiological brain activity representative of various mental states, and not to artifacts, such as those arising from naturalistic movements. In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data were collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifactual component of the EEG signal is significantly more informative than brain activity with respect to classification accuracy. This finding is consistent across different feature extraction methods and classification pipelines. While informative brain signals can be recovered with suitable cleaning procedures, we recommend that features should not be designed solely to maximize classification accuracy, as this could select for remaining artifactual components. We also propose the use of machine learning approaches that are interpretable to verify that classification is driven by physiological brain states. In summary, whereas informative artifacts are a helpful friend in BCI-based communication applications, they can be a problematic foe in the estimation of physiological brain states.},
}
@article {pmid35008333,
year = {2021},
author = {Montegut, C and Correard, F and Nouguerède, E and Rey, D and Chevalier, T and Meurer, M and Deville, JL and Baciuchka, M and Pradel, V and Greillier, L and Villani, P and Couderc, AL},
title = {Prognostic Value of the B12/CRP Index in Older Systemically Treatable Cancer Patients.},
journal = {Cancers},
volume = {14},
number = {1},
pages = {},
pmid = {35008333},
issn = {2072-6694},
abstract = {BACKGROUND: While comprehensive geriatric assessment (CGA) in older patients treated for cancer assesses several related domains, it does not include standardized biological tests. The present study aimed to: (1) assess the prognosis value of the B12/CRP index (BCI) in a population of systemically treatable older patients with cancer and (2) analyze the association between BCI value and pre-existing geriatric frailty.<br><br>METHOD: We conducted a retrospective observational study between January 2016 and June 2020 at Marseille University Hospital. All consecutive cancer patients aged 70 years and over before initiating systemic therapy were included.<br><br>RESULTS: Of the 863 patients included, 60.5% were men and 42.5% had metastatic stage cancer. Mean age was 81 years. The low-BCI group (≤10,000) had a significantly longer survival time than the mid-BCI (10,000 < BCI ≤ 40,000) and high-BCI (BCI > 40,000) groups (HR = 0.327, CI95% [0.26-0.42], p-value = 0.0001). Mid- and high-BCI (BCI > 40,000) values were associated with impaired functional status and malnutrition.<br><br>CONCLUSION: A BCI > 10,000 would appear to be a good biological prognostic factor for poor survival times and pre-existing geriatric impairment in older cancer patients before they initiate systemic treatment.},
}
@article {pmid35008079,
year = {2022},
author = {Liu, Y and Wang, Z and Huang, S and Wang, W and Ming, D},
title = {EEG characteristic investigation of the sixth-finger motor imagery and optimal channel selection for classification.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac49a6},
pmid = {35008079},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Hand ; Humans ; Imagery, Psychotherapy ; Imagination ; },
abstract = {Objective.Supernumerary robotic limbs are body augmentation robotic devices by adding extra limbs or fingers to the human body different from the traditional wearable robotic devices such as prosthesis and exoskeleton. We proposed a novel motor imagery (MI)-based brain-computer interface (BCI) paradigm based on the sixth-finger which imagines controlling the extra finger movements. The goal of this work is to investigate the electromyographic (EEG) characteristics and the application potential of MI-based BCI systems based on the new imagination paradigm (the sixth finger MI).Approach.Fourteen subjects participated in the experiment involving the sixth finger MI tasks and rest state. Event-related spectral perturbation was adopted to analyze EEG spatial features and key-channel time-frequency features. Common spatial patterns were used for feature extraction and classification was implemented by support vector machine. A genetic algorithm (GA) was used to select combinations of EEG channels that maximized classification accuracy and verified EEG patterns based on the sixth finger MI. And we conducted a longitudinal 4 weeks EEG control experiment based on the new paradigm.Main results.Event-related desynchronization (ERD) was found in the supplementary motor area and primary motor area with a faint contralateral dominance. Unlike traditional MI based on the human hand, ERD was also found in frontal lobe. GA results showed that the distribution of the optimal eight-channel is similar to EEG topographical distributions, nearing parietal and frontal lobe. And the classification accuracy based on the optimal eight-channel (the highest accuracy of 80% and mean accuracy of 70%) was significantly better than that based on the random eight-channel (p< 0.01).Significance.This work provided a new paradigm for MI-based MI system and verified its feasibility, widened the control bandwidth of the BCI system.},
}
@article {pmid35007548,
year = {2022},
author = {Moon, J and Orlandi, S and Chau, T},
title = {A comparison and classification of oscillatory characteristics in speech perception and covert speech.},
journal = {Brain research},
volume = {1781},
number = {},
pages = {147778},
doi = {10.1016/j.brainres.2022.147778},
pmid = {35007548},
issn = {1872-6240},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Speech ; *Speech Perception ; Wavelet Analysis ; },
abstract = {Covert speech, the mental imagery of speaking, has been studied increasingly to understand and decode thoughts in the context of brain-computer interfaces. In studies of speech comprehension, neural oscillations are thought to play a key role in the temporal encoding of speech. However, little is known about the role of oscillations in covert speech. In this study, we investigated the oscillatory involvements in covert speech and speech perception. Data were collected from 10 participants with 64 channel EEG. Participants heard the words, 'blue' and 'orange', and subsequently mentally rehearsed them. First, continuous wavelet transform was performed on epoched signals and subsequently two-tailed t-tests between two classes (tasks) were conducted to determine statistical differences in frequency and time (t-CWT). In the current experiment, a task comprised speech perception or covert rehearsal of a word while a condition was the discrimination between tasks. Features were extracted using t-CWT and subsequently classified using a support vector machine. θ and γ phase amplitude coupling (PAC) was also assessed within tasks and across conditions between perception and covert activities (i.e. cross-task). All binary classifications accuracies (80-90%) significantly exceeded chance level, supporting the use of t-CWT in determining relative oscillatory involvements. While the perception condition dynamically invoked all frequencies with more prominent θ and α activity, the covert condition favoured higher frequencies with significantly higher γ activity than perception. Moreover, the perception condition produced significant θ-γ PAC, possibly corroborating a reported linkage between syllabic and phonemic sampling. Although this coupling was found to be suppressed in the covert condition, we found significant cross-task coupling between perception θ and covert speech γ. Covert speech processing appears to be largely associated with higher frequencies of EEG. Importantly, the significant cross-task coupling between speech perception and covert speech, in the absence of within-task covert speech PAC, seems to support the notion that the γ- and θ-bands reflect, respectively, shared and unique encoding processes across tasks.},
}
@article {pmid35006823,
year = {2021},
author = {Mathur, V and Talapatra, S and Kar, S and Hennighausen, Z},
title = {In Vivo Partial Restoration of Neural Activity across Severed Mouse Spinal Cord Bridged with Ultralong Carbon Nanotubes.},
journal = {ACS applied bio materials},
volume = {4},
number = {5},
pages = {4071-4078},
doi = {10.1021/acsabm.1c00248},
pmid = {35006823},
issn = {2576-6422},
mesh = {Animals ; Biocompatible Materials/*chemistry ; Materials Testing ; Mice ; Nanotubes, Carbon/*chemistry ; Neurons/*metabolism ; Particle Size ; Spinal Cord/*metabolism ; },
abstract = {Electrically bridging severed nerves in vivo has transformative healthcare implications, but current materials are inadequate. Carbon nanotubes (CNTs) are promising, with low impedance, high charge injection capacity, high flexibility, are chemically inert, and can electrically couple to neurons. Ultralong CNTs are unexplored for neural applications. Using only ultralong CNTs in saline, without neuroregeneration or rehabilitation, we partially restored neural activity across a severed mouse spinal cord, recovering 23.8% of the intact amplitude, while preserving signal shape. Neural signals are preferentially facilitated over artifact signals by a factor of ×5.2, suggesting ultralong CNTs are a promising material for neural-scaffolding and neural-electronics applications.},
}
@article {pmid35006782,
year = {2021},
author = {Li, X and Song, Y and Xiao, G and He, E and Xie, J and Dai, Y and Xing, Y and Wang, Y and Wang, Y and Xu, S and Wang, M and Tao, TH and Cai, X},
title = {PDMS-Parylene Hybrid, Flexible Micro-ECoG Electrode Array for Spatiotemporal Mapping of Epileptic Electrophysiological Activity from Multicortical Brain Regions.},
journal = {ACS applied bio materials},
volume = {4},
number = {11},
pages = {8013-8022},
doi = {10.1021/acsabm.1c00923},
pmid = {35006782},
issn = {2576-6422},
mesh = {Brain/physiology ; Dimethylpolysiloxanes ; Electrodes ; *Epilepsy/diagnosis ; Humans ; *Nanotubes, Carbon ; Polymers ; Xylenes ; },
abstract = {Epilepsy detection and focus location are urgent issues that need to be solved in epilepsy research. A cortex conformable and fine spatial accuracy electrocorticogram (ECoG) sensor array, especially for real-time detection of multicortical functional regions and delineating epileptic focus remains a challenge. Here, we fabricated a polydimethylsiloxane (PDMS)-parylene hybrid, flexible micro-ECoG electrode array. The multiwalled carbon nanotubes (MWCNTs)/poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) nanocomposite-modified electrode interface significantly improved the sensing performance with low impedance (20.68 ± 6.65 kΩ), stable phase offset, and high sensitivity. The electrophysiological activities of multicortical brain regions (somatosensory cortex, parietal association cortex, and visual cortex) were simultaneously monitored during normal and epileptic statuses. The epileptic ECoG activities spread spatiotemporally from the starting point toward the adjacent cortex. Significant variations of the waveform, power, and frequency band were observed. The ECoG potential (123 ± 23 μV) at normal status was prominently up to 417 ± 87 μV at the spike wave stage. Besides, the power for epileptic activity (11.049 ± 4.513 μW) was 10 times higher than that (1.092 ± 0.369 μW) for normal activity. In addition, the theta frequency band was found to be a characteristic frequency band of epileptic signals. These joint analysis results of multicortical regions indicated that the active micron-scale region on the parietal association cortex was more likely to be the epileptogenic focus. Cortical mapping with high spatial detail provides the accurate delineation of lesions. The flexible micro-ECoG electrode array is a powerful tool for constructing a spatiotemporal map of the cortex. It provides a technical platform for epileptic focus location, biomedical diagnosis, and brain-computer interaction.},
}
@article {pmid35005771,
year = {2022},
author = {Kugler, EC and Frost, J and Silva, V and Plant, K and Chhabria, K and Chico, TJA and Armitage, PA},
title = {Zebrafish vascular quantification: a tool for quantification of three-dimensional zebrafish cerebrovascular architecture by automated image analysis.},
journal = {Development (Cambridge, England)},
volume = {149},
number = {3},
pages = {},
pmid = {35005771},
issn = {1477-9129},
support = {IG/15/1/31328/BHF_/British Heart Foundation/United Kingdom ; },
mesh = {Animals ; Animals, Genetically Modified/growth &amp; development ; Automation ; Brain/blood supply ; Cerebral Veins/*diagnostic imaging ; Cluster Analysis ; Embryo, Nonmammalian/blood supply ; Embryonic Development ; Image Processing, Computer-Assisted ; Imaging, Three-Dimensional/*methods ; User-Computer Interface ; Zebrafish/*growth &amp; development ; },
abstract = {Zebrafish transgenic lines and light sheet fluorescence microscopy allow in-depth insights into three-dimensional vascular development in vivo. However, quantification of the zebrafish cerebral vasculature in 3D remains highly challenging. Here, we describe and test an image analysis workflow for 3D quantification of the total or regional zebrafish brain vasculature, called zebrafish vasculature quantification (ZVQ). It provides the first landmark- or object-based vascular inter-sample registration of the zebrafish cerebral vasculature, producing population average maps allowing rapid assessment of intra- and inter-group vascular anatomy. ZVQ also extracts a range of quantitative vascular parameters from a user-specified region of interest, including volume, surface area, density, branching points, length, radius and complexity. Application of ZVQ to 13 experimental conditions, including embryonic development, pharmacological manipulations and morpholino-induced gene knockdown, shows that ZVQ is robust, allows extraction of biologically relevant information and quantification of vascular alteration, and can provide novel insights into vascular biology. To allow dissemination, the code for quantification, a graphical user interface and workflow documentation are provided. Together, ZVQ provides the first open-source quantitative approach to assess the 3D cerebrovascular architecture in zebrafish.},
}
@article {pmid35002899,
year = {2021},
author = {Ziadeh, H and Gulyas, D and Nielsen, LD and Lehmann, S and Nielsen, TB and Kjeldsen, TKK and Hougaard, BI and Jochumsen, M and Knoche, H},
title = {"Mine Works Better": Examining the Influence of Embodiment in Virtual Reality on the Sense of Agency During a Binary Motor Imagery Task With a Brain-Computer Interface.},
journal = {Frontiers in psychology},
volume = {12},
number = {},
pages = {806424},
pmid = {35002899},
issn = {1664-1078},
abstract = {Motor imagery-based brain-computer interfaces (MI-BCI) have been proposed as a means for stroke rehabilitation, which combined with virtual reality allows for introducing game-based interactions into rehabilitation. However, the control of the MI-BCI may be difficult to obtain and users may face poor performance which frustrates them and potentially affects their motivation to use the technology. Decreases in motivation could be reduced by increasing the users' sense of agency over the system. The aim of this study was to understand whether embodiment (ownership) of a hand depicted in virtual reality can enhance the sense of agency to reduce frustration in an MI-BCI task. Twenty-two healthy participants participated in a within-subject study where their sense of agency was compared in two different embodiment experiences: 1) avatar hand (with body), or 2) abstract blocks. Both representations closed with a similar motion for spatial congruency and popped a balloon as a result. The hand/blocks were controlled through an online MI-BCI. Each condition consisted of 30 trials of MI-activation of the avatar hand/blocks. After each condition a questionnaire probed the participants' sense of agency, ownership, and frustration. Afterwards, a semi-structured interview was performed where the participants elaborated on their ratings. Both conditions supported similar levels of MI-BCI performance. A significant correlation between ownership and agency was observed (r = 0.47, p = 0.001). As intended, the avatar hand yielded much higher ownership than the blocks. When controlling for performance, ownership increased sense of agency. In conclusion, designers of BCI-based rehabilitation applications can draw on anthropomorphic avatars for the visual mapping of the trained limb to improve ownership. While not While not reducing frustration ownership can improve perceived agency given sufficient BCI performance. In future studies the findings should be validated in stroke patients since they may perceive agency and ownership differently than able-bodied users.},
}
@article {pmid35002658,
year = {2021},
author = {Chang, Y and He, C and Tsai, BY and Ko, LW},
title = {Multi-Parameter Physiological State Monitoring in Target Detection Under Real-World Settings.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {785562},
pmid = {35002658},
issn = {1662-5161},
abstract = {Mental state changes induced by stimuli under experimental settings or by daily events in real life affect task performance and are entwined with physical and mental health. In this study, we developed a physiological state indicator with five parameters that reflect the subject's real-time physiological states based on online EEG signal processing. These five parameters are attention, fatigue, stress, and the brain activity shifts of the left and right hemispheres. We designed a target detection experiment modified by a cognitive attention network test for validating the effectiveness of the proposed indicator, as such conditions would better approximate a real chaotic environment. Results demonstrated that attention levels while performing the target detection task were significantly higher than during rest periods, but also exhibited a decay over time. In contrast, the fatigue level increased gradually and plateaued by the third rest period. Similar to attention levels, the stress level decreased as the experiment proceeded. These parameters are therefore shown to be highly correlated to different stages of the experiment, suggesting their usage as primary factors in passive brain-computer interfaces (BCI). In addition, the left and right brain activity indexes reveal the EEG neural modulations of the corresponding hemispheres, which set a feasible reference of activation for an active BCI control system, such as one executing motor imagery tasks. The proposed indicator is applicable to potential passive and active BCI applications for monitoring the subject's physiological state change in real-time, along with providing a means of evaluating the associated signal quality to enhance the BCI performance.},
}
@article {pmid34997274,
year = {2022},
author = {Perrault, JR and Page-Karjian, A and Morgan, AN and Burns, LK and Stacy, NI},
title = {Morphometrics and blood analytes of leatherback sea turtle hatchlings (Dermochelys coriacea) from Florida: reference intervals, temporal trends with clutch deposition date, and body size correlations.},
journal = {Journal of comparative physiology. B, Biochemical, systemic, and environmental physiology},
volume = {192},
number = {2},
pages = {313-324},
pmid = {34997274},
issn = {1432-136X},
mesh = {Animals ; Body Size ; Florida ; Reference Values ; Seasons ; *Turtles/physiology ; },
abstract = {The northwest Atlantic leatherback sea turtle (Dermochelys coriacea) population is exhibiting decreasing trends along numerous nesting beaches. Since population health and viability are inherently linked, it is important to establish species- and life-stage class-specific blood analyte reference intervals (RIs) so that effects of future disturbances on organismal health can be better understood. For hatchling leatherbacks, the objectives of this study were to (1) establish RIs for morphometrics and blood analytes; (2) evaluate correlations between hatchling morphometrics, blood analytes, and hatching success; and (3) determine temporal trends in hatchling morphometrics and blood analytes across nesting season. Blood samples were collected from 176 naturally emerging leatherback hatchlings from 18 clutches. Reference intervals were established for morphometrics and blood analytes. Negative relationships were noted between hatchling mass and packed cell volume, total white blood cells, heterophils, lymphocytes, and total protein and between body condition index (BCI) and immature red blood cells (RBC), RBC polychromasia and anisocytosis, and total protein. Clutch deposition date showed positive relationships with lymphocytes and total protein, and negative relationships with hatchling mass and BCI. Hatching success was positively correlated with mass, and negatively with total protein and glucose, suggesting that nutritional provisions in eggs, incubation time, and/or metabolic rates could change later in the season and affect survivorship. These various observed correlations provide evidence for increased physiological stress (e.g., inflammation, subclinical dehydration) in hatchlings emerging later in nesting season, presumably due to increased nest temperatures or other environmental factors (e.g., moisture/rainfall). Data reported herein provide morphometric and blood analyte data for leatherback hatchlings and will allow for future investigations into spatiotemporal trends and responses to various stressors.},
}
@article {pmid34996515,
year = {2022},
author = {Endris, BS and Dinant, GJ and Gebreyesus, SH and Spigt, M},
title = {Risk factors of anemia among preschool children in Ethiopia: a Bayesian geo-statistical model.},
journal = {BMC nutrition},
volume = {8},
number = {1},
pages = {2},
pmid = {34996515},
issn = {2055-0928},
abstract = {BACKGROUND: The etiology and risk factors of anemia are multifactorial and varies across context. Due to the geospatial clustering of anemia, identifying risk factors for anemia should account for the geographic variability. Failure to adjust for spatial dependence whilst identifying risk factors of anemia could give spurious association. We aimed to identify risk factors of anemia using a Bayesian geo-statistical model.<br><br>METHODS: We analyzed the Ethiopian Demographic and Health Survey (EDHS) 2016 data. The sample was selected using a stratified, two- stage cluster sampling design. In this survey, 9268 children had undergone anemia testing. Hemoglobin level was measured using a HemoCue photometer and the results were recorded onsite. Based on the World Health Organization's cut-off points, a child was considered anaemic if their altitude adjusted haemoglobin (Hb) level was less than 11 g/dL. Risk factors for anemia were identified using a Bayesian geo-statistical model, which accounted for spatial dependency structure in the data. Posterior means and 95% credible interval (BCI) were used to report our findings. We used a statistically significant level at 0.05.<br><br>RESULT: The 9267 children in our study were between 6 and 59 months old. Fifty two percent (52%) of children were males. Thirteen percent (13%) of children were from the highest wealth quintile whereas 23% from the lowest wealth quintile. Most of them lived in rural areas (90%). The overall prevalence of anemia among preschool children was 57% (95% CI: 54.4-59.4). We found that child stunting (OR = 1.26, 95% BCI (1.14-1.39), wasting (OR = 1.35, 95% BCI (1.15-1.57), maternal anemia (OR = 1.61, 95% BCI (1.44-1.79), mothers having two under five children (OR = 1.2, 95% BCI (1.08-1.33) were risk factors associated with anemia among preschool children. Children from wealthy households had lower risk of anemia (AOR = 0.73, 95% BCI (0.62-0.85).<br><br>CONCLUSION: Using the Bayesian geospatial statistical modeling, we were able to account for spatial dependent structure in the data, which minimize spurious association. Childhood Malnutrition, maternal anemia, increased fertility, and poor wealth status were risk factors of anemia among preschool children in Ethiopia. The existing anaemia control programs such as IFA supplementation during pregnancy should be strengthened to halt intergenerational effect of anaemia. Furthermore, routine childhood anaemia screening and intervention program should be part of the Primary health care in Ethiopia.},
}
@article {pmid34996051,
year = {2022},
author = {Zheng, L and Pei, W and Gao, X and Zhang, L and Wang, Y},
title = {A high-performance brain switch based on code-modulated visual evoked potentials.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac494f},
pmid = {34996051},
issn = {1741-2552},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials ; *Evoked Potentials, Visual ; Photic Stimulation ; },
abstract = {Objective.Asynchronous brain-computer interfaces (BCIs) are more practical and natural compared to synchronous BCIs. A brain switch is a standard asynchronous BCI, which can automatically detect the specified change of the brain and discriminate between the control state and the idle state. The current brain switches still face challenges on relatively long reaction time (RT) and high false positive rate (FPR).Approach.In this paper, an online electroencephalography-based brain switch is designed to realize a fast reaction and keep long idle time (IDLE) without false positives (FPs) using code-modulated visual evoked potentials (c-VEPs). Two stimulation paradigms were designed and compared in the experiments: multi-code concatenate modulation (concatenation mode) and single-code periodic modulation (periodic mode). Using a task-related component analysis-based detection algorithm, EEG data can be decoded into a series of code indices. Brain states can be detected by a template matching approach with a sliding window on the output series.Main results.The online experiments achieved an averageRTof 1.49 s when the averageIDLEfor eachFPwas 68.57 min (1.46 × 10-2FP min-1) or an averageRTof 1.67 s withoutFPs. Significance.This study provides a practical c-VEP based brain switch system with both fast reaction and low FPR during idle state, which can be used in various BCI applications.},
}
@article {pmid34995902,
year = {2022},
author = {Wang, X and Wang, M and Sheng, H and Zhu, L and Zhu, J and Zhang, H and Liu, Y and Zhan, L and Wang, X and Zhang, J and Wu, X and Suo, Z and Xi, W and Wang, H},
title = {Subdural neural interfaces for long-term electrical recording, optical microscopy and magnetic resonance imaging.},
journal = {Biomaterials},
volume = {281},
number = {},
pages = {121352},
doi = {10.1016/j.biomaterials.2021.121352},
pmid = {34995902},
issn = {1878-5905},
mesh = {*Brain/diagnostic imaging ; Elastomers ; Electrodes, Implanted ; Hydrogels ; Magnetic Resonance Imaging ; Metals ; *Microscopy ; },
abstract = {Though commonly used, metal electrodes are incompatible with brain tissues, often leading to injury and failure to achieve long-term implantation. Here we report a subdural neural interface of hydrogel functioning as an ionic conductor, and elastomer as a dielectric. We demonstrate that it incurs a far less glial reaction and less cerebrovascular destruction than a metal electrode. Using a cat model, the hydrogel electrode was able to record electrical signals comparably in quality to a metal electrode. The hydrogel-elastomer neural interface also readily facilitated multimodal functions. Both the hydrogel and elastomer are transparent, enabling in vivo optical microscopy. For imaging, cerebral vessels and calcium signals were imaged using two-photon microscopy. The new electrode is compatible with magnetic resonance imaging and does not cause artifact images. Such a new multimodal neural interface could represent immediate opportunity for use in broad areas of application in neuroscience research and clinical neurology.},
}
@article {pmid34994497,
year = {2022},
author = {Nafees, M and Ullah, S and Ahmed, I},
title = {Modulation of drought adversities in Vicia faba by the application of plant growth promoting rhizobacteria and biochar.},
journal = {Microscopy research and technique},
volume = {85},
number = {5},
pages = {1856-1869},
doi = {10.1002/jemt.24047},
pmid = {34994497},
issn = {1097-0029},
mesh = {Charcoal ; *Droughts ; Ecosystem ; *Vicia faba ; },
abstract = {Drought is the greatest threat to world food security, seen as the catalyst for the great famines of the past. Given that the world's water supply is limited, it is likely that future demand of food for increasing population will further exacerbate the drought effects. Therefore, the present study was aimed to investigate the effect of biochar and plant growth promoting rhizobacteria (PGPR) Sphingobacterium pakistanensis (NCCP246) and Cellulomonas pakistanensis (NCCP11) on agronomic and physiological attributes of Vicia faba two varieties Desi (V1) and Pulista (V2) under induced drought stress. The seeds were sown in earthen pots filled with 3 kg sand and soil (1:2), and biochar (0 and 5% w/w) in triplicate arranged in complete randomized design. Analysis of biochar possessed 0.49 g cm-3 bulk density, 9.6 pH; 5.4 cmol kg-1 cation exchange capacity, 3.64% organic carbon and EC 6.7 ds/m. Agronomic attributes including seed LAI, LAR, SVI, %PHSI and RWC were improved by 30.4-180.4%, 14.37-47.20%, 37.64-50.91%, 18.21-30.80, and 35.82-54.34% in both varieties by the co-application of biochar and PGPR. Stomatal physiology and epidermal vigor was successfully improved by the application of PGPR and biochar as analyzed by scanning electron microscopy (SEM). Photosynthetic pigments, flavonoids, phenols, proline and glycine betaine were amplified by 58.33-173.8%, 50.59-130.33%, 46.58-86.62%, 46.66-109.30%, 35.74-56.10%, and 21.96-77.22% in both varieties by the co-application of biochar and PGPR. So, the present work concluded that, combined application of biochar and PGPR could be an effective strategy to alleviate the adversities of drought in V. faba growing in drastic ecosystems.},
}
@article {pmid34990366,
year = {2022},
author = {Ravi, A and Lu, J and Pearce, S and Jiang, N},
title = {Enhanced System Robustness of Asynchronous BCI in Augmented Reality Using Steady-State Motion Visual Evoked Potential.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {85-95},
doi = {10.1109/TNSRE.2022.3140772},
pmid = {34990366},
issn = {1558-0210},
mesh = {Algorithms ; *Augmented Reality ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Photic Stimulation/methods ; },
abstract = {This study evaluated the effect of change in background on steady state visually evoked potentials (SSVEP) and steady state motion visually evoked potentials (SSMVEP) based brain computer interfaces (BCI) in a small-profile augmented reality (AR) headset. A four target SSVEP and SSMVEP BCI was implemented using the Cognixion AR headset prototype. An active (AB) and a non-active background (NB) were evaluated. The signal characteristics and classification performance of the two BCI paradigms were studied. Offline analysis was performed using canonical correlation analysis (CCA) and complex-spectrum based convolutional neural network (C-CNN). Finally, the asynchronous pseudo-online performance of the SSMVEP BCI was evaluated. Signal analysis revealed that the SSMVEP stimulus was more robust to change in background compared to SSVEP stimulus in AR. The decoding performance revealed that the C-CNN method outperformed CCA for both stimulus types and NB background, in agreement with results in the literature. The average offline accuracies for W = 1 s of C-CNN were (NB vs. AB): SSVEP: 82% ±15% vs. 60% ±21% and SSMVEP: 71.4% ± 22% vs. 63.5% ± 18%. Additionally, for W = 2 s, the AR-SSMVEP BCI with the C-CNN method was 83.3% ± 27% (NB) and 74.1% ±22% (AB). The results suggest that with the C-CNN method, the AR-SSMVEP BCI is both robust to change in background conditions and provides high decoding accuracy compared to the AR-SSVEP BCI. This study presents novel results that highlight the robustness and practical application of SSMVEP BCIs developed with a low-cost AR headset.},
}
@article {pmid34987165,
year = {2022},
author = {Wang, Y and Zhou, S and Qi, X and Yang, F and Maurer, MJ and Habermann, TM and Witzig, TE and Wang, ML and Nowakowski, GS},
title = {Efficacy of front-line immunochemotherapy for follicular lymphoma: a network meta-analysis of randomized controlled trials.},
journal = {Blood cancer journal},
volume = {12},
number = {1},
pages = {1},
pmid = {34987165},
issn = {2044-5385},
mesh = {Antineoplastic Combined Chemotherapy Protocols/*therapeutic use ; Bayes Theorem ; Cyclophosphamide/therapeutic use ; Doxorubicin/therapeutic use ; Humans ; *Immunotherapy/methods ; Lymphoma, Follicular/*therapy ; Maintenance Chemotherapy ; Prednisone/therapeutic use ; Progression-Free Survival ; Randomized Controlled Trials as Topic ; Rituximab/therapeutic use ; Treatment Outcome ; Vincristine/therapeutic use ; },
abstract = {Front-line treatment for follicular lymphoma has evolved with the introduction of maintenance therapy, bendamustine (Benda), obinutuzumab (G), and lenalidomide (Len). We conducted a random-effects Bayesian network meta-analysis (NMA) of phase 3 randomized controlled trials (RCTs) to identify the regimens with superior efficacy. Progression-free survival (PFS) was compared between 11 modern regimens with different immunochemotherapy and maintenance strategies. G-Benda-G resulted in with the best PFS, with an HR of 0.41 compared to R-Benda, a surface under the cumulative ranking curve (SUCRA) of 0.97, a probability of being the best treatment (PbBT) of 72%, and a posterior ranking distribution (PoRa) of 1 (95% BCI 1-3). This was followed by R-Benda-R4 (HR = 0.49, PbBT = 25%, PoRa = 2) and R-Benda-R (HR = 0.60, PbBT = 3%, PoRa = 3). R-CHOP-R (HR = 0.96) and R-Len-R (HR = 0.97) had similar efficacy to R-Benda. Bendamustine was a better chemotherapy backbone than CHOP either with maintenance (R-Benda-R vs R-CHOP-R, HR = 0.62; G-Benda-G vs G-CHOP-G, HR = 0.55) or without maintenance therapy (R-Benda vs R-CHOP, HR = 0.68). Rituximab maintenance improved PFS following R-CHOP (R-CHOP-R vs R-CHOP, HR = 0.65) or R-Benda (R-Benda-R vs R-Benda, HR = 0.60; R-Benda-R4 vs R-Benda, HR = 0.49). In the absence of multi-arm RCTs that include all common regimens, this NMA provides an important and useful guide to inform treatment decisions.},
}
@article {pmid34986601,
year = {2022},
author = {Xu, M and Bai, X and Ai, B and Zhang, G and Song, C and Zhao, J and Wang, Y and Wei, L and Qian, F and Li, Y and Zhou, X and Zhou, L and Yang, Y and Chen, J and Liu, J and Shang, D and Wang, X and Zhao, Y and Huang, X and Zheng, Y and Zhang, J and Wang, Q and Li, C},
title = {TF-Marker: a comprehensive manually curated database for transcription factors and related markers in specific cell and tissue types in human.},
journal = {Nucleic acids research},
volume = {50},
number = {D1},
pages = {D402-D412},
pmid = {34986601},
issn = {1362-4962},
mesh = {Bone and Bones/chemistry/metabolism ; Brain/metabolism ; Colon/chemistry/metabolism ; *Databases, Genetic ; Female ; Gene Expression Regulation ; Genetic Markers ; Humans ; Internet ; Liver/chemistry/metabolism ; Lung/chemistry/metabolism ; Male ; Mammary Glands, Human/chemistry/metabolism ; Molecular Sequence Annotation ; Neoplasms/*genetics/metabolism/pathology ; Organ Specificity ; Prostate/chemistry/metabolism ; *Software ; Transcription Factors/classification/*genetics/metabolism ; *Transcription, Genetic ; },
abstract = {Transcription factors (TFs) play key roles in biological processes and are usually used as cell markers. The emerging importance of TFs and related markers in identifying specific cell types in human diseases increases the need for a comprehensive collection of human TFs and related markers sets. Here, we developed the TF-Marker database (TF-Marker, http://bio.liclab.net/TF-Marker/), aiming to provide cell/tissue-specific TFs and related markers for human. By manually curating thousands of published literature, 5905 entries including information about TFs and related markers were classified into five types according to their functions: (i) TF: TFs which regulate expression of the markers; (ii) T Marker: markers which are regulated by the TF; (iii) I Marker: markers which influence the activity of TFs; (iv) TFMarker: TFs which play roles as markers and (v) TF Pmarker: TFs which play roles as potential markers. The 5905 entries of TF-Marker include 1316 TFs, 1092 T Markers, 473 I Markers, 1600 TFMarkers and 1424 TF Pmarkers, involving 383 cell types and 95 tissue types in human. TF-Marker further provides a user-friendly interface to browse, query and visualize the detailed information about TFs and related markers. We believe TF-Marker will become a valuable resource to understand the regulation patterns of different tissues and cells.},
}
@article {pmid34986475,
year = {2022},
author = {Chen, J and Yi, W and Wang, D and Du, J and Fu, L and Li, T},
title = {FB-CGANet: filter bank channel group attention network for multi-class motor imagery classification.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac4852},
pmid = {34986475},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; *Imagination ; Neural Networks, Computer ; },
abstract = {Objective.Motor imagery-based brain-computer interface (MI-BCI) is one of the most important BCI paradigms and can identify the target limb of subjects from the feature of MI-based Electroencephalography signals. Deep learning methods, especially lightweight neural networks, provide an efficient technique for MI decoding, but the performance of lightweight neural networks is still limited and need further improving. This paper aimed to design a novel lightweight neural network for improving the performance of multi-class MI decoding.Approach.A hybrid filter bank structure that can extract information in both time and frequency domain was proposed and combined with a novel channel attention method channel group attention (CGA) to build a lightweight neural network Filter Bank CGA Network (FB-CGANet). Accompanied with FB-CGANet, the band exchange data augmentation method was proposed to generate training data for networks with filter bank structure.Main results.The proposed method can achieve higher 4-class average accuracy (79.4%) than compared methods on the BCI Competition IV IIa dataset in the experiment on the unseen evaluation data. Also, higher average accuracy (93.5%) than compared methods can be obtained in the cross-validation experiment.Significance.This work implies the effectiveness of channel attention and filter bank structure in lightweight neural networks and provides a novel option for multi-class motor imagery classification.},
}
@article {pmid34986110,
year = {2022},
author = {Wang, Z and Zhang, J and Zhang, X and Chen, P and Wang, B},
title = {Transformer Model for Functional Near-Infrared Spectroscopy Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2022.3140531},
pmid = {34986110},
issn = {2168-2208},
abstract = {Functional near-infrared spectroscopy (fNIRS) is a promising neuroimaging technology. The fNIRS classification problem has always been the focus of the brain-computer interface (BCI). Inspired by the success of Transformer based on self-attention mechanism in the fields of natural language processing and computer vision, we propose an fNIRS classification network based on Transformer, named fNIRS-T. We explore the spatial-level and channel-level representation of fNIRS signals to improve data utilization and network representation capacity. Besides, a preprocessing module, which consists of one-dimensional average pooling and layer normalization, is designed to replace filtering and baseline correction of data preprocessing. It makes fNIRS-T an end-to-end network, called fNIRS-PreT. Compared with traditional machine learning classifiers, convolutional neural network (CNN), and long short-term memory (LSTM), the proposed models obtain the best accuracy on three open-access datasets. Specifically, in the most extensive ternary classification task (30 subjects) that includes three types of overt movements, fNIRS-T, CNN, and LSTM obtain 75.49%, 72.89%, and 61.94% on test sets, respectively. Compared to traditional classifiers, fNIRS-T is at least 27.41% higher than statistical features and 6.79% higher than well-designed features. In the individual subject experiment of the ternary classification task, fNIRS-T achieves an average subject accuracy of 78.22% and surpasses CNN and LSTM by a large margin of +4.75% and +11.33%. fNIRS-PreT using raw data also achieves competitive performance to fNIRS-T. Therefore, the proposed models improve the performance of fNIRS-based BCI significantly.},
}
@article {pmid34983830,
year = {2022},
author = {Imbrosci, B and Schmitz, D and Orlando, M},
title = {Automated Detection and Localization of Synaptic Vesicles in Electron Microscopy Images.},
journal = {eNeuro},
volume = {9},
number = {1},
pages = {},
pmid = {34983830},
issn = {2373-2822},
mesh = {Animals ; Humans ; Mice ; Microscopy, Electron ; Presynaptic Terminals ; Synapses ; *Synaptic Vesicles ; *Zebrafish ; },
abstract = {Information transfer and integration in the brain occurs at chemical synapses and is mediated by the fusion of synaptic vesicles filled with neurotransmitter. Synaptic vesicle dynamic spatial organization regulates synaptic transmission as well as synaptic plasticity. Because of their small size, synaptic vesicles require electron microscopy (EM) for their imaging, and their analysis is conducted manually. The manual annotation and segmentation of the hundreds to thousands of synaptic vesicles, is highly time consuming and limits the throughput of data collection. To overcome this limitation, we built an algorithm, mainly relying on convolutional neural networks (CNNs), capable of automatically detecting and localizing synaptic vesicles in electron micrographs. The algorithm was trained on murine synapses but we show that it works well on synapses from different species, ranging from zebrafish to human, and from different preparations. As output, we provide the vesicle count and coordinates, the nearest neighbor distance (nnd) and the estimate of the vesicles area. We also provide a graphical user interface (GUI) to guide users through image analysis, result visualization, and manual proof-reading. The application of our algorithm is especially recommended for images produced by transmission EM. Since this type of imaging is used routinely to investigate presynaptic terminals, our solution will likely be of interest for numerous research groups.},
}
@article {pmid34982594,
year = {2022},
author = {Andersen, RA and Aflalo, T and Bashford, L and Bjånes, D and Kellis, S},
title = {Exploring Cognition with Brain-Machine Interfaces.},
journal = {Annual review of psychology},
volume = {73},
number = {},
pages = {131-158},
doi = {10.1146/annurev-psych-030221-030214},
pmid = {34982594},
issn = {1545-2085},
mesh = {Brain ; *Brain-Computer Interfaces ; Cerebral Cortex ; Cognition ; Humans ; Parietal Lobe ; },
abstract = {Traditional brain-machine interfaces decode cortical motor commands to control external devices. These commands are the product of higher-level cognitive processes, occurring across a network of brain areas, that integrate sensory information, plan upcoming motor actions, and monitor ongoing movements. We review cognitive signals recently discovered in the human posterior parietal cortex during neuroprosthetic clinical trials. These signals are consistent with small regions of cortex having a diverse role in cognitive aspects of movement control and body monitoring, including sensorimotor integration, planning, trajectory representation, somatosensation, action semantics, learning, and decision making. These variables are encoded within the same population of cells using structured representations that bind related sensory and motor variables, an architecture termed partially mixed selectivity. Diverse cognitive signals provide complementary information to traditional motor commands to enable more natural and intuitive control of external devices.},
}
@article {pmid34981404,
year = {2022},
author = {Kerley, CI and Chaganti, S and Nguyen, TQ and Bermudez, C and Cutting, LE and Beason-Held, LL and Lasko, T and Landman, BA},
title = {pyPheWAS: A Phenome-Disease Association Tool for Electronic Medical Record Analysis.},
journal = {Neuroinformatics},
volume = {},
number = {},
pages = {},
pmid = {34981404},
issn = {1559-0089},
abstract = {Along with the increasing availability of electronic medical record (EMR) data, phenome-wide association studies (PheWAS) and phenome-disease association studies (PheDAS) have become a prominent, first-line method of analysis for uncovering the secrets of EMR. Despite this recent growth, there is a lack of approachable software tools for conducting these analyses on large-scale EMR cohorts. In this article, we introduce pyPheWAS, an open-source python package for conducting PheDAS and related analyses. This toolkit includes 1) data preparation, such as cohort censoring and age-matching; 2) traditional PheDAS analysis of ICD-9 and ICD-10 billing codes; 3) PheDAS analysis applied to a novel EMR phenotype mapping: current procedural terminology (CPT) codes; and 4) novelty analysis of significant disease-phenotype associations found through PheDAS. The pyPheWAS toolkit is approachable and comprehensive, encapsulating data prep through result visualization all within a simple command-line interface. The toolkit is designed for the ever-growing scale of available EMR data, with the ability to analyze cohorts of 100,000 + patients in less than 2 h. Through a case study of Down Syndrome and other intellectual developmental disabilities, we demonstrate the ability of pyPheWAS to discover both known and potentially novel disease-phenotype associations across different experiment designs and disease groups. The software and user documentation are available in open source at https://github.com/MASILab/pyPheWAS .},
}
@article {pmid34979193,
year = {2022},
author = {Wang, Y and Luo, Z and Zhao, S and Xie, L and Xu, M and Ming, D and Yin, E},
title = {Spatial localization in target detection based on decoding N2pc component.},
journal = {Journal of neuroscience methods},
volume = {369},
number = {},
pages = {109440},
doi = {10.1016/j.jneumeth.2021.109440},
pmid = {34979193},
issn = {1872-678X},
mesh = {Brain ; *Brain-Computer Interfaces ; Cognition ; Electroencephalography/methods ; Humans ; Photic Stimulation/methods ; Recognition, Psychology ; },
abstract = {BACKGROUND: The Gaze-independent BCI system is used to restore communication in patients with eye movement disorders. One available control mechanism is the utilization of spatial attention. However, spatial information is mostly used to simply answer the "True/False" target recognition question and is seldom used to improve the efficiency of target detection. Therefore, it is necessary to utilize the potential advantages of spatial attention to improving the target detection efficiency.<br><br>NEW METHOD: We found that N2pc could be used to assess spatial attention shift and determine target position. It was a negative wave in the posterior brain on the contralateral target stimulus. From this, we designed a novel spatial coding paradigm to achieve two main purposes at each stimulus presentation: target recognition and spatial localization.<br><br>We used a two-step classification framework to decode the P300 and N2pc components.<br><br>RESULTS: The average decoding accuracy of fourteen subjects was 84.43% (σ = 1.14%), and the classification accuracy of six subjects was more than 85%. The information transfer rate of the spatial coding paradigm could reach 60.52 bits/min. Compared with the single stimulus paradigm, the target detection efficiency was successfully improved by approximately 10%.<br><br>CONCLUSIONS: The spatial coding paradigm proposed in this paper answered both "True/False" and "Left/Right" questions by decoding spatial attention information. This method could significantly improve image detection efficiencies, such as visual search tasks, Internet image screening, or military target determination.},
}
@article {pmid34977321,
year = {2022},
author = {Sykes, AL and Larrieu, E and Poggio, TV and Céspedes, MG and Mujica, GB and Basáñez, MG and Prada, JM},
title = {Modelling diagnostics for Echinococcus granulosus surveillance in sheep using Latent Class Analysis: Argentina as a case study.},
journal = {One health (Amsterdam, Netherlands)},
volume = {14},
number = {},
pages = {100359},
pmid = {34977321},
issn = {2352-7714},
support = {MR/R015600/1/MRC_/Medical Research Council/United Kingdom ; },
abstract = {Echinococcus granulosus sensu lato is a globally prevalent zoonotic parasitic cestode leading to cystic echinococcosis (CE) in both humans and sheep with both medical and financial impacts, whose reduction requires the application of a One Health approach to its control. Regarding the animal health component of this approach, lack of accurate and practical diagnostics in livestock impedes the assessment of disease burden and the implementation and evaluation of control strategies. We use of a Bayesian Latent Class Analysis (LCA) model to estimate ovine CE prevalence in sheep samples from the Río Negro province of Argentina accounting for uncertainty in the diagnostics. We use model outputs to evaluate the performance of a novel recombinant B8/2 antigen B subunit (rEgAgB8/2) indirect enzyme-linked immunosorbent assay (ELISA) for detecting E. granulosus in sheep. Necropsy (as a partial gold standard), western blot (WB) and ELISA diagnostic data were collected from 79 sheep within two Río Negro slaughterhouses, and used to estimate individual infection status (assigned as a latent variable within the model). Using the model outputs, the performance of the novel ELISA at both individual and flock levels was evaluated, respectively, using a receiver operating characteristic (ROC) curve, and simulating a range of sample sizes and prevalence levels within hypothetical flocks. The estimated (mean) prevalence of ovine CE was 27.5% (95%Bayesian credible interval (95%BCI): 13.8%-58.9%) within the sample population. At the individual level, the ELISA had a mean sensitivity and specificity of 55% (95%BCI: 46%-68%) and 68% (95%BCI: 63%-92%), respectively, at an optimal optical density (OD) threshold of 0.378. At the flock level, the ELISA had an 80% probability of correctly classifying infection at an optimal cut-off threshold of 0.496. These results suggest that the novel ELISA could play a useful role as a flock-level diagnostic for CE surveillance in the region, supplementing surveillance activities in the human population and thus strengthening a One Health approach. Importantly, selection of ELISA cut-off threshold values must be tailored according to the epidemiological situation.},
}
@article {pmid34976324,
year = {2021},
author = {Liu, Y and Chen, C and Belkacem, AN and Wang, Z and Cheng, L and Wang, C and Chang, Y and Li, P},
title = {Motor Imagination of Lower Limb Movements at Different Frequencies.},
journal = {Journal of healthcare engineering},
volume = {2021},
number = {},
pages = {4073739},
pmid = {34976324},
issn = {2040-2309},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Imagination ; Lower Extremity ; Movement ; },
abstract = {Motor imagination (MI) is the mental process of only imagining an action without an actual movement. Research on MI has made significant progress in feature information detection and machine learning decoding algorithms, but there are still problems, such as a low overall recognition rate and large differences in individual execution effects, which make the development of MI run into a bottleneck. Aiming at solving this bottleneck problem, the current study optimized the quality of the MI original signal by "enhancing the difficulty of imagination tasks," conducted the qualitative and quantitative analyses of EEG rhythm characteristics, and used quantitative indicators, such as ERD mean value and recognition rate. Research on the comparative analysis of the lower limb MI of different tasks, namely, high-frequency motor imagination (HFMI) and low-frequency motor imagination (LFMI), was conducted. The results validate the following: the average ERD of HFMI (-1.827) is less than that of LFMI (-1.3487) in the alpha band, so did (-3.4756 < -2.2891) in the beta band. In the alpha and beta characteristic frequency bands, the average ERD of HFMI is smaller than that of LFMI, and the ERD values of the two are significantly different (p=0.0074 < 0.01; r = 0.945). The ERD intensity STD values of HFMI are less than those of LFMI. which suggests that the ERD intensity individual difference among the subjects is smaller in the HFMI mode than in the LFMI mode. The average recognition rate of HFMI is higher than that of LFMI (87.84% > 76.46%), and the recognition rate of the two modes is significantly different (p=0.0034 < 0.01; r = 0.429). In summary, this research optimizes the quality of MI brain signal sources by enhancing the difficulty of imagination tasks, achieving the purpose of improving the overall recognition rate of the lower limb MI of the participants and reducing the differences of individual execution effects and signal quality among the subjects.},
}
@article {pmid34976039,
year = {2021},
author = {Zhang, S and Sun, L and Mao, X and Hu, C and Liu, P},
title = {Review on EEG-Based Authentication Technology.},
journal = {Computational intelligence and neuroscience},
volume = {2021},
number = {},
pages = {5229576},
pmid = {34976039},
issn = {1687-5273},
mesh = {*Biometric Identification ; Biometry ; *Brain-Computer Interfaces ; Electroencephalography ; Technology ; },
abstract = {With the rapid development of brain-computer interface technology, as a new biometric feature, EEG signal has been widely concerned in recent years. The safety of brain-computer interface and the long-term insecurity of biometric authentication have a new solution. This review analyzes the biometrics of EEG signals, and the latest research is involved in the authentication process. This review mainly introduced the method of EEG-based authentication and systematically introduced EEG-based biometric cryptosystems for authentication for the first time. In cryptography, the key is the core basis of authentication in the cryptographic system, and cryptographic technology can effectively improve the security of biometric authentication and protect biometrics. The revocability of EEG-based biometric cryptosystems is an advantage that traditional biometric authentication does not have. Finally, the existing problems and future development directions of identity authentication technology based on EEG signals are proposed, providing a reference for the related studies.},
}
@article {pmid34975445,
year = {2021},
author = {Koelewijn, AD and Audu, M and Del-Ama, AJ and Colucci, A and Font-Llagunes, JM and Gogeascoechea, A and Hnat, SK and Makowski, N and Moreno, JC and Nandor, M and Quinn, R and Reichenbach, M and Reyes, RD and Sartori, M and Soekadar, S and Triolo, RJ and Vermehren, M and Wenger, C and Yavuz, US and Fey, D and Beckerle, P},
title = {Adaptation Strategies for Personalized Gait Neuroprosthetics.},
journal = {Frontiers in neurorobotics},
volume = {15},
number = {},
pages = {750519},
pmid = {34975445},
issn = {1662-5218},
abstract = {Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.},
}
@article {pmid34975434,
year = {2021},
author = {He, C and Liu, J and Zhu, Y and Du, W},
title = {Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {765525},
pmid = {34975434},
issn = {1662-5161},
abstract = {Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development.},
}
@article {pmid34972343,
year = {2021},
author = {Shang, B and Shang, P},
title = {Multivariate synchronization curve: A measure of synchronization in different multivariate signals.},
journal = {Chaos (Woodbury, N.Y.)},
volume = {31},
number = {12},
pages = {123121},
doi = {10.1063/5.0064807},
pmid = {34972343},
issn = {1089-7682},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; },
abstract = {As a method to measure the synchronization between two different sets of signals, the multivariate synchronization index (MSI) has played an irreplaceable role in the field of frequency recognition of brain-computer interface since it was proposed. On this basis, we make a generalization of MSI by using the escort distribution to replace the original distribution. In this way, MSI can be converted from a determined value to the multivariate synchronization curve, which will vary as the parameter q of the escort distribution changes. Numerical experiments are carried out on both simulated and real-world data to confirm the effectiveness of this new method. Compared with the case of MSI (i.e., q = 1), the extended form of MSI proposed in this article can obviously capture the relationship between signals more comprehensively, implying that it is a more perfect method to describe the synchronization between them. The results reveal that this method can not only effectively extract the important information contained in different signals, but also has the potential to become a practical synchronization measurement method of multivariate signals.},
}
@article {pmid34971597,
year = {2022},
author = {Zhou, L and Zhu, Q and Wu, B and Qin, B and Hu, H and Qian, Z},
title = {A comparison of directed functional connectivity among fist-related brain activities during movement imagery, movement execution, and movement observation.},
journal = {Brain research},
volume = {1777},
number = {},
pages = {147769},
doi = {10.1016/j.brainres.2021.147769},
pmid = {34971597},
issn = {1872-6240},
mesh = {Adult ; Brain/*physiology ; Brain-Computer Interfaces ; Connectome ; Electroencephalography ; Female ; Functional Laterality/*physiology ; Hand/physiology ; Humans ; Imagery, Psychotherapy ; Imagination/*physiology ; Male ; Motor Cortex ; Movement/*physiology ; Nervous System Physiological Phenomena ; },
abstract = {Brain-computer interface (BCI) has been widely used in sports training and rehabilitation training. It is primarily based on action simulation, including movement imagery (MI) and movement observation (MO). However, the development of BCI technology is limited due to the challenge of getting an in-depth understanding of brain networks involved in MI, MO, and movement execution (ME). To better understand the brain activity changes and the communications across various brain regions under MO, ME, and MI, this study conducted the fist experiment under MO, ME, and MI. We recorded 64-channel electroencephalography (EEG) from 39 healthy subjects (25 males, 14 females, all right-handed) during fist tasks, obtained intensities and locations of sources using EEG source imaging (ESI), computed source activation modes, and finally investigated the brain networks using spectral Granger causality (GC). The brain regions involved in the three motor conditions are similar, but the degree of participation of each brain region and the network connections among the brain regions are different. MO, ME, and MI did not recruit shared brain connectivity networks. In addition, both source activation modes and brain network connectivity had lateralization advantages.},
}
@article {pmid34970111,
year = {2021},
author = {Zhou, Y and Hu, L and Yu, T and Li, Y},
title = {A BCI-Based Study on the Relationship Between the SSVEP and Retinal Eccentricity in Overt and Covert Attention.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {746146},
pmid = {34970111},
issn = {1662-4548},
abstract = {Covert attention aids us in monitoring the environment and optimizing performance in visual tasks. Past behavioral studies have shown that covert attention can enhance spatial resolution. However, electroencephalography (EEG) activity related to neural processing between central and peripheral vision has not been systematically investigated. Here, we conducted an EEG study with 25 subjects who performed covert attentional tasks at different retinal eccentricities ranging from 0.75° to 13.90°, as well as tasks involving overt attention and no attention. EEG signals were recorded with a single stimulus frequency to evoke steady-state visual evoked potentials (SSVEPs) for attention evaluation. We found that the SSVEP response in fixating at the attended location was generally negatively correlated with stimulus eccentricity as characterized by Euclidean distance or horizontal and vertical distance. Moreover, more pronounced characteristics of SSVEP analysis were also acquired in overt attention than in covert attention. Furthermore, offline classification of overt attention, covert attention, and no attention yielded an average accuracy of 91.42%. This work contributes to our understanding of the SSVEP representation of attention in humans and may also lead to brain-computer interfaces (BCIs) that allow people to communicate with choices simply by shifting their attention to them.},
}
@article {pmid34969148,
year = {2022},
author = {Ruiz, S and Virseda-Chamorro, M and Salinas, J and Queissert, F and Arance, I and Angulo, JC},
title = {Influence of ATOMS implant on the voiding phase of patients with post-prostatectomy urinary incontinence.},
journal = {Neurourology and urodynamics},
volume = {41},
number = {2},
pages = {609-615},
doi = {10.1002/nau.24856},
pmid = {34969148},
issn = {1520-6777},
mesh = {Humans ; Male ; Prospective Studies ; Prostatectomy/adverse effects ; *Urinary Bladder Neck Obstruction/etiology/surgery ; *Urinary Incontinence/complications ; Urination ; Urodynamics ; },
abstract = {OBJECTIVE: To assess changes in voiding phase, especially urethral resistance after post-prostatectomy urinary incontinence (PPI) treatment with the Adjustable TransObturator Male System (ATOMS).<br><br>MATERIAL AND METHODS: A longitudinal prospective study was performed on 45 men treated with ATOMS for PPI, with the intention to evaluate the changes produced by the implant on the voiding phase. Patients with preoperative urodynamic study were offered postoperative urodynamic evaluation, and both studies were compared. The following urodynamic date were evaluated: maximum voiding detrusor pressure, detrusor pressure at maximum flow rate, maximum flow rate (Qmax), voiding volume, post-void residue, bladder outlet obstruction index (BOOI), urethral resistance factor (URA), and bladder contractility index (BCI). The statistical analysis used were the mean comparison test for dependent groups (Student's t test) for parametric variables and the Wilcoxon test for non-parametric variables. The signification level was set at 95% bilateral.<br><br>RESULTS: A total of 37 patients (82.2%) used zero pads/day at the time of urodynamic postoperative evaluation and pad-test evolved from 592 ± 289 ml baseline to 25 ± 40 ml (p = 0.0001). Significant differences were observed in Qmax (15 ± 8.3 before and 11 ± 8.3 after surgery; p = 0.008), voiding volume (282 ± 130.7 before and 184 ± 99.92 after surgery). BOOI (-12 ± 23.9 before and -2 ± 21.4 after surgery; p = 0.025) and BCI (93 ± 46.4 before and 76 ± 46.0 after surgery; p = 0.044). In no case did we observe postoperative bladder outlet obstruction, according to URA parameter below 29 cm H2 O in all cases. There was not a significant variation either in post-void urinary residual volume (15 ± 47.4 before and 14 ± 24.2 after surgery, p = 0.867).<br><br>CONCLUSIONS: The ATOMS implant induces a decrease of Qmax, voided volume, and bladder contractility and an increase of BOOI. However, our findings suggest that ATOMS device does not cause bladder outlet obstruction.},
}
@article {pmid34969088,
year = {2021},
author = {Bibián, C and Irastorza-Landa, N and Schönauer, M and Birbaumer, N and López-Larraz, E and Ramos-Murguialday, A},
title = {On the Extraction of Purely Motor EEG Neural Correlates during an Upper Limb Visuomotor Task.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {},
number = {},
pages = {},
doi = {10.1093/cercor/bhab479},
pmid = {34969088},
issn = {1460-2199},
support = {2452-0-0/2//Fortüne-Program of the University of Tübingen/ ; FKZ-16SV7754//Bundesministerium für Bildung und Forschung/ ; V5GR2001M1007-01//REHOME/ ; 01QE2023C E! 113928//Eurostars/ ; KK 2016/00083//Basque Government Science Program/ ; //Basque Government and IKERBASQUE, Basque Foundation for Science/ ; },
abstract = {Deciphering and analyzing the neural correlates of different movements from the same limb using electroencephalography (EEG) would represent a notable breakthrough in the field of sensorimotor neurophysiology. Functional movements involve concurrent posture co-ordination and head and eye movements, which create electrical activity that affects EEG recordings. In this paper, we revisit the identification of brain signatures of different reaching movements using EEG and present, test, and validate a protocol to separate the effect of head and eye movements from a reaching task-related visuomotor brain activity. Ten healthy participants performed reaching movements under two different conditions: avoiding head and eye movements and moving with no constrains. Reaching movements can be identified from EEG with unconstrained eye and head movement, whereas the discriminability of the signals drops to chance level otherwise. These results show that neural patterns associated with different arm movements could only be extracted from EEG if the eye and head movements occurred concurrently with the task, polluting the recordings. Although these findings do not imply that brain correlates of reaching directions cannot be identified from EEG, they show the consequences that ignoring these events can have in any EEG study that includes a visuomotor task.},
}
@article {pmid34966976,
year = {2021},
author = {Mederos, A and Galarraga, D and van der Graaf-van Bloois, L and Buczinski, S},
title = {Performance of bovine genital campylobacteriosis diagnostic tests in bulls from Uruguay: a Bayesian latent class model approach.},
journal = {Tropical animal health and production},
volume = {54},
number = {1},
pages = {32},
pmid = {34966976},
issn = {1573-7438},
support = {FSSA_X_2014_1_105894//agencia nacional de investigación e innovación de uruguay/ ; CL_37//instituto nacional de investigación agropecuaria/ ; },
mesh = {Animals ; Bayes Theorem ; *Campylobacter Infections/diagnosis/epidemiology/veterinary ; Campylobacter fetus/genetics ; Cattle ; *Cattle Diseases/diagnosis/epidemiology ; Diagnostic Tests, Routine ; Genitalia ; Latent Class Analysis ; Male ; Real-Time Polymerase Chain Reaction/veterinary ; Sensitivity and Specificity ; Uruguay ; },
abstract = {The sensitivity (Se) and specificity (Sp) of three diagnostic tests for the detection of Campylobacter fetus venerealis (Cfv) using field samples were estimated using a Bayesian latent class model (BLCM), accounting for the absence of a gold standard. The tests included in this study were direct immunofluorescence antibody test (IFAT), polymerase chain reaction (PCR), and real-time PCR (RT-PCR). Twelve farms from two different populations were selected and bull prepuce samples were collected. The IFAT was performed according to the OIE Manual. The conventional PCR was performed as multiplex, targeting the gene nahE for C. fetus species identification and insertion element ISCfe1 for Cfv identification. The RT-PCR was performed as uniplex: one targeting the gene nahE for C. fetus and the other targeting the insertion ISCfe1 (ISC2) for Cfv. Results from the BLCM showed a median Se of 11.7% (Bayesian credibility interval (BCI): 1.93-29.79%), 53.7% (BCI: 23.1-95.0%), and 36.1% (BCI: 14.5-71.7%) for IFAT, PCR, and RT-PCR respectively. The Sp were 94.5% (BCI: 90.1-97.9%), 97.0% (BCI: 92.9-99.3%), and 98.4% (BCI: 95.3-99.7%) for IFAT, PCR, and RT-PCR respectively. The correlation between PCR and RT-PCR was positive and low in samples from both sampled population (0.63% vs 8.47%). These results suggest that diagnostic sensitivity of the studied tests is lower using field samples than using pure Cfv strains.},
}
@article {pmid34966524,
year = {2021},
author = {Li, C and Wei, J and Huang, X and Duan, Q and Zhang, T},
title = {Effects of a Brain-Computer Interface-Operated Lower Limb Rehabilitation Robot on Motor Function Recovery in Patients with Stroke.},
journal = {Journal of healthcare engineering},
volume = {2021},
number = {},
pages = {4710044},
pmid = {34966524},
issn = {2040-2309},
mesh = {Activities of Daily Living ; *Brain-Computer Interfaces ; Diffusion Tensor Imaging ; Humans ; Lower Extremity ; Recovery of Function ; *Robotics ; *Stroke/diagnostic imaging ; *Stroke Rehabilitation ; Upper Extremity ; },
abstract = {Purpose: To observe the effect of a brain-computer interface-operated lower limb rehabilitation robot (BCI-LLRR) on functional recovery from stroke and to explore mechanisms.<br><br>Methods: Subacute-phase stroke patients were randomly divided into two groups. In addition to the routine intervention, patients in the treatment group trained on the BCI-LLRR and underwent the lower limb pedal training in the control group, both for the same time (30 min/day). All patients underwent assessment by instruments such as the National Institutes of Health Stroke Scale (NIHSS) and the Fugl-Meyer upper and lower limb motor function and balance tests, at 2 and 4 weeks of treatment and at 3 months after the end of treatment. Patients were also tested before treatment and after 4 weeks by leg motor evoked potential (MEP) and diffusion tensor imaging/tractography (DTI/DTT) of the head.<br><br>Results: After 4 weeks, the Fugl-Meyer leg function and NIHSS scores were significantly improved in the treatment group vs. controls (P < 0.01). At 3 months, further significant improvement was observed. The MEP amplitude and latency of the treatment group were significantly improved vs. controls. The effect of treatment on fractional anisotropy values was not significant.<br><br>Conclusions: The BCI-LLRR promoted leg functional recovery after stroke and improved activities of daily living, possibly by improving cerebral-cortex excitability and white matter connectivity.},
}
@article {pmid34965212,
year = {2021},
author = {Xu, C and Neuroth, TA and Fujiwara, T and Liang, R and Ma, KL},
title = {A Predictive Visual Analytics System for Studying Neurodegenerative Disease based on DTI Fiber Tracts.},
journal = {IEEE transactions on visualization and computer graphics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TVCG.2021.3137174},
pmid = {34965212},
issn = {1941-0506},
abstract = {Diffusion tensor imaging (DTI) has been used to study the effects of neurodegenerative diseases on neural pathways, which may lead to more reliable and early diagnosis of these diseases as well as a better understanding of how they affect the brain. We introduce a predictive visual analytics system for studying patient groups based on their labeled DTI fiber tract data and corresponding statistics. The systems machine-learning-augmented interface guides the user through an organized and holistic analysis space, including the statistical feature space, the physical space, and the space of patients over different groups. We use a custom machine learning pipeline to help narrow down this large analysis space and then explore it pragmatically through a range of linked visualizations. We conduct several case studies using DTI and T1-weighted images from the research database of Parkinsons Progression Markers Initiative.},
}
@article {pmid34964297,
year = {2021},
author = {Becsei, Á and Solymosi, N and Csabai, I and Magyar, D},
title = {Detection of antimicrobial resistance genes in urban air.},
journal = {MicrobiologyOpen},
volume = {10},
number = {6},
pages = {e1248},
pmid = {34964297},
issn = {2045-8827},
mesh = {*Air Microbiology ; Bacteria/*genetics ; Cities ; Drug Resistance, Bacterial/*genetics ; *Genes, Bacterial ; Metagenome ; *Microbiota ; Sensitivity and Specificity ; },
abstract = {To understand antibiotic resistance in pathogenic bacteria, we need to monitor environmental microbes as reservoirs of antimicrobial resistance genes (ARGs). These bacteria are present in the air and can be investigated with the whole metagenome shotgun sequencing approach. This study aimed to investigate the feasibility of a method for metagenomic analysis of microbial composition and ARGs in the outdoor air. Air samples were collected with a Harvard impactor in the PM10 range at 50 m from a hospital in Budapest. From the DNA yielded from samples of PM10 fraction single-end reads were generated with an Ion Torrent sequencer. During the metagenomic analysis, reads were classified taxonomically. The core bacteriome was defined. Reads were assembled to contigs and the ARG content was analyzed. The dominant genera in the core bacteriome were Bacillus, Acinetobacter, Leclercia and Paenibacillus. Among the identified ARGs best hits were vanRA, Bla1, mphL, Escherichia coli EF-Tu mutants conferring resistance to pulvomycin; BcI, FosB, and mphM. Despite the low DNA content of the samples of PM10 fraction, the number of detected airborne ARGs was surprisingly high.},
}
@article {pmid34962871,
year = {2022},
author = {Jin, J and Sun, H and Daly, I and Li, S and Liu, C and Wang, X and Cichocki, A},
title = {A Novel Classification Framework Using the Graph Representations of Electroencephalogram for Motor Imagery Based Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {20-29},
doi = {10.1109/TNSRE.2021.3139095},
pmid = {34962871},
issn = {1558-0210},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; },
abstract = {The motor imagery (MI) based brain-computer interfaces (BCIs) have been proposed as a potential physical rehabilitation technology. However, the low classification accuracy achievable with MI tasks is still a challenge when building effective BCI systems. We propose a novel MI classification model based on measurement of functional connectivity between brain regions and graph theory. Specifically, motifs describing local network structures in the brain are extracted from functional connectivity graphs. A graph embedding model called Ego-CNNs is then used to build a classifier, which can convert the graph from a structural representation to a fixed-dimensional vector for detecting critical structure in the graph. We validate our proposed method on four datasets, and the results show that our proposed method produces high classification accuracies in two-class classification tasks (92.8% for dataset 1, 93.4% for dataset 2, 96.5% for dataset 3, and 80.2% for dataset 4) and multiclass classification tasks (90.33% for dataset 1). Our proposed method achieves a mean Kappa value of 0.88 across nine participants, which is superior to other methods we compared it to. These results indicate that there is a local structural difference in functional connectivity graphs extracted under different motor imagery tasks. Our proposed method has great potential for motor imagery classification in future studies.},
}
@article {pmid34961366,
year = {2021},
author = {Aydemir, O and Saka, K and Ozturk, M},
title = {Investigating the effects of stimulus duration and inter-stimulus interval parameters on P300 based BCI application performance.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-9},
doi = {10.1080/10255842.2021.2022127},
pmid = {34961366},
issn = {1476-8259},
abstract = {The main goal of electroencephalography (EEG) based brain-computer interface (BCI) research is to develop a fast and higher classification accuracy (CA) rate method than those of existing ones. Generally, in BCI applications, either motor imagery or event-related P300 based techniques are used for data recording. The stimulus duration (SD) and the inter-stimulus interval (ISI) are crucial two parameters directly affecting the decision speed of the BCI system. In this study, we investigated the performance of the P300 based application in terms of speed and CA for three kinds of protocols which are called fast, medium, and slow included different SD and the ISI values. The training and test data sets were recorded in one week of delay from 8 subjects. The features were extracted by standard deviation, variance, mean, Wavelet Transform and Fourier Transform techniques. Afterwards, they were classified by the k-nearest neighbor algorithm. We obtained 87.08%, 85.41% and 83.95% average CA rate for the fast, medium, and slow protocols, respectively. The obtained results showed that the proposed fast protocol method achieved CA rate between 78.33% and 93.33%. Based on the obtained results, it can be concluded that the fast protocol values can be used for establishing a more accurate and faster P300 based BCI.},
}
@article {pmid34960597,
year = {2021},
author = {Rossi, F and Savi, F and Prestia, A and Mongardi, A and Demarchi, D and Buccino, G},
title = {Combining Action Observation Treatment with a Brain-Computer Interface System: Perspectives on Neurorehabilitation.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {24},
pages = {},
pmid = {34960597},
issn = {1424-8220},
mesh = {Activities of Daily Living ; *Brain-Computer Interfaces ; Child ; Female ; Humans ; Male ; *Neurological Rehabilitation ; *Stroke Rehabilitation ; Upper Extremity ; },
abstract = {Action observation treatment (AOT) exploits a neurophysiological mechanism, matching an observed action on the neural substrates where that action is motorically represented. This mechanism is also known as mirror mechanism. In a typical AOT session, one can distinguish an observation phase and an execution phase. During the observation phase, the patient observes a daily action and soon after, during the execution phase, he/she is asked to perform the observed action at the best of his/her ability. Indeed, the execution phase may sometimes be difficult for those patients where motor impairment is severe. Although, in the current practice, the physiotherapist does not intervene on the quality of the execution phase, here, we propose a stimulation system based on neurophysiological parameters. This perspective article focuses on the possibility to combine AOT with a brain-computer interface system (BCI) that stimulates upper limb muscles, thus facilitating the execution of actions during a rehabilitation session. Combining a rehabilitation tool that is well-grounded in neurophysiology with a stimulation system, such as the one proposed, may improve the efficacy of AOT in the treatment of severe neurological patients, including stroke patients, Parkinson's disease patients, and children with cerebral palsy.},
}
@article {pmid34960469,
year = {2021},
author = {Hag, A and Handayani, D and Altalhi, M and Pillai, T and Mantoro, T and Kit, MH and Al-Shargie, F},
title = {Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {24},
pages = {},
pmid = {34960469},
issn = {1424-8220},
support = {funding grant by Deanship of Scientific Research, Taif University Researchers Supporting Project number. TURSP-2020 / 300), Taif University,Taif, Saudi Arabia//Taif University/ ; },
mesh = {*Algorithms ; *Electroencephalography ; Recognition, Psychology ; Stress, Psychological/diagnosis ; Support Vector Machine ; },
abstract = {In real-life applications, electroencephalogram (EEG) signals for mental stress recognition require a conventional wearable device. This, in turn, requires an efficient number of EEG channels and an optimal feature set. This study aims to identify an optimal feature subset that can discriminate mental stress states while enhancing the overall classification performance. We extracted multi-domain features within the time domain, frequency domain, time-frequency domain, and network connectivity features to form a prominent feature vector space for stress. We then proposed a hybrid feature selection (FS) method using minimum redundancy maximum relevance with particle swarm optimization and support vector machines (mRMR-PSO-SVM) to select the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art metaheuristic methods. The proposed model significantly reduced the features vector space by an average of 70% compared with the state-of-the-art methods while significantly increasing overall detection performance.},
}
@article {pmid34960399,
year = {2021},
author = {Covantes-Osuna, C and López, JB and Paredes, O and Vélez-Pérez, H and Romo-Vázquez, R},
title = {Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {24},
pages = {},
pmid = {34960399},
issn = {1424-8220},
support = {This work was supported by the Consejo Nacional de Ciencia y Tecnología - CONACyT [Scholarship to C.C.O scholarship 480527, O.P. with CVU 713526 and J.B.L. with CVU 745514]//Consejo Nacional de Ciencia y Tecnología/ ; },
mesh = {Brain ; *Brain-Computer Interfaces ; *Electroencephalography ; Imagery, Psychotherapy ; Imagination ; },
abstract = {The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer modeling, in contrast, makes it possible to build whole-brain models by integrating features of various kinds. The aim of this work was to analyze EEG dynamics studies while gathering motor imagery data through single-layer and multilayer network modeling. The motor imagery database used consists of 18 EEG recordings of four motor imagery tasks: left hand, right hand, feet, and tongue. Brain connectivity was estimated by calculating the coherence adjacency matrices from each electrophysiological band (δ, θ, α and β) from brain areas and then embedding them by considering each band as a single-layer graph and a layer of the multilayer brain models. Constructing a reliable multilayer network topology requires a threshold that distinguishes effective connections from spurious ones. For this reason, two thresholds were implemented, the classic fixed (average) one and Otsu's version. The latter is a new proposal for an adaptive threshold that offers reliable insight into brain topology and dynamics. Findings from the brain network models suggest that frontal and parietal brain regions are involved in motor imagery tasks.},
}
@article {pmid34960373,
year = {2021},
author = {Mezzina, G and Annese, VF and De Venuto, D},
title = {A Cybersecure P300-Based Brain-to-Computer Interface against Noise-Based and Fake P300 Cyberattacks.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {24},
pages = {},
pmid = {34960373},
issn = {1424-8220},
mesh = {Algorithms ; Artificial Intelligence ; Brain ; *Brain-Computer Interfaces ; Computers ; Electroencephalography ; Event-Related Potentials, P300 ; },
abstract = {In a progressively interconnected world where the Internet of Things (IoT), ubiquitous computing, and artificial intelligence are leading to groundbreaking technology, cybersecurity remains an underdeveloped aspect. This is particularly alarming for brain-to-computer interfaces (BCIs), where hackers can threaten the user's physical and psychological safety. In fact, standard algorithms currently employed in BCI systems are inadequate to deal with cyberattacks. In this paper, we propose a solution to improve the cybersecurity of BCI systems. As a case study, we focus on P300-based BCI systems using support vector machine (SVM) algorithms and EEG data. First, we verified that SVM algorithms are incapable of identifying hacking by simulating a set of cyberattacks using fake P300 signals and noise-based attacks. This was achieved by comparing the performance of several models when validated using real and hacked P300 datasets. Then, we implemented our solution to improve the cybersecurity of the system. The proposed solution is based on an EEG channel mixing approach to identify anomalies in the transmission channel due to hacking. Our study demonstrates that the proposed architecture can successfully identify 99.996% of simulated cyberattacks, implementing a dedicated counteraction that preserves most of BCI functions.},
}
@article {pmid34960261,
year = {2021},
author = {Ascari, L and Marchenkova, A and Bellotti, A and Lai, S and Moro, L and Koshmak, K and Mantoan, A and Barsotti, M and Brondi, R and Avveduto, G and Sechi, D and Compagno, A and Avanzini, P and Ambeck-Madsen, J and Vecchiato, G},
title = {Validation of a Novel Wearable Multistream Data Acquisition and Analysis System for Ergonomic Studies.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {24},
pages = {},
pmid = {34960261},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Ergonomics ; Humans ; Systems Analysis ; *Wearable Electronic Devices ; },
abstract = {Nowadays, the growing interest in gathering physiological data and human behavior in everyday life scenarios is paralleled by an increase in wireless devices recording brain and body signals. However, the technical issues that characterize these solutions often limit the full brain-related assessments in real-life scenarios. Here we introduce the Biohub platform, a hardware/software (HW/SW) integrated wearable system for multistream synchronized acquisitions. This system consists of off-the-shelf hardware and state-of-art open-source software components, which are highly integrated into a high-tech low-cost solution, complete, yet easy to use outside conventional labs. It flexibly cooperates with several devices, regardless of the manufacturer, and overcomes the possibly limited resources of recording devices. The Biohub was validated through the characterization of the quality of (i) multistream synchronization, (ii) in-lab electroencephalographic (EEG) recordings compared with a medical-grade high-density device, and (iii) a Brain-Computer-Interface (BCI) in a real driving condition. Results show that this system can reliably acquire multiple data streams with high time accuracy and record standard quality EEG signals, becoming a valid device to be used for advanced ergonomics studies such as driving, telerehabilitation, and occupational safety.},
}
@article {pmid34959509,
year = {2021},
author = {Zheng, S and Wu, J and Hu, Z and Gan, M and Liu, L and Song, C and Lei, Y and Wang, H and Liao, L and Feng, Y and Shao, Y and Ruan, Y and Xing, H},
title = {Epidemiology and Molecular Transmission Characteristics of HIV in the Capital City of Anhui Province in China.},
journal = {Pathogens (Basel, Switzerland)},
volume = {10},
number = {12},
pages = {},
pmid = {34959509},
issn = {2076-0817},
support = {2017ZX10201101002-004//National Science and Technology Major Project/ ; 11971479//National Natural Science Foundation of China/ ; },
abstract = {Hefei, Anhui province, is one of the cities in the Yangtze River Delta, where many people migrate to Jiangsu, Zhejiang and Shanghai. High migration also contributes to the HIV epidemic. This study explored the HIV prevalence in Hefei to provide a reference for other provinces and assist in the prevention and control of HIV in China. A total of 816 newly reported people with HIV in Hefei from 2017 to 2020 were recruited as subjects. HIV subtypes were identified by a phylogenetic tree. The most prevalent subtypes were CRF07_BC (41.4%), CRF01_AE (38.1%) and CRF55_01B (6.3%). Molecular networks were inferred using HIV-TRACE. The largest and most active transmission cluster was CRF55_01B in Hefei's network. A Chinese national database (50,798 sequences) was also subjected to molecular network analysis to study the relationship between patients in Hefei and other provinces. CRF55_01B and CRF07_BC-N had higher clustered and interprovincial transmission rates in the national molecular network. People with HIV in Hefei mainly transmitted the disease within the province. Finally, we displayed the epidemic trend of HIV in Hefei in recent years with the dynamic change of effective reproductive number (Re). The weighted overall Re increased rapidly from 2012 to 2015, with a peak value of 3.20 (95% BCI, 2.18-3.85). After 2015, Re began to decline and remained stable at around 1.80. In addition, the Re of CRF55_01B was calculated to be between 2.0 and 4.0 in 2018 and 2019. More attention needs to be paid to the rapid spread of CRF55_01B and CRF07_BC-N strains among people with HIV and the high Re in Hefei. These data provide necessary support to guide the targeted prevention and control of HIV.},
}
@article {pmid34958737,
year = {2022},
author = {Pitt, KM and Dietz, A},
title = {Applying Implementation Science to Support Active Collaboration in Noninvasive Brain-Computer Interface Development and Translation for Augmentative and Alternative Communication.},
journal = {American journal of speech-language pathology},
volume = {31},
number = {1},
pages = {515-526},
doi = {10.1044/2021_AJSLP-21-00152},
pmid = {34958737},
issn = {1558-9110},
mesh = {*Brain-Computer Interfaces ; Communication ; *Communication Aids for Disabled ; Humans ; Implementation Science ; },
abstract = {PURPOSE: The purpose of this article is to consider how, alongside engineering advancements, noninvasive brain-computer interface (BCI) for augmentative and alternative communication (AAC; BCI-AAC) developments can leverage implementation science to increase the clinical impact of this technology. We offer the Consolidated Framework for Implementation Research (CFIR) as a structure to help guide future BCI-AAC research. Specifically, we discuss CFIR primary domains that include intervention characteristics, the outer and inner settings, the individuals involved in the intervention, and the process of implementation, alongside pertinent subdomains including adaptability, cost, patient needs and recourses, implementation climate, other personal attributes, and the process of engaging. The authors support their view with current citations from both the AAC and BCI-AAC fields.<br><br>CONCLUSIONS: The article aimed to provide thoughtful considerations for how future research may leverage the CFIR to support meaningful BCI-AAC translation for those with severe physical impairments. We believe that, although significant barriers to BCI-AAC development still exist, incorporating implementation research may be timely for the field of BCI-AAC and help account for diversity in end users, navigate implementation obstacles, and support a smooth and efficient translation of BCI-AAC technology. Moreover, the sooner clinicians, individuals who use AAC, their support networks, and engineers collectively improve BCI-AAC outcomes and the efficiency of translation, the sooner BCI-AAC may become an everyday tool in the AAC arsenal.},
}
@article {pmid34958261,
year = {2022},
author = {Nojima, I and Sugata, H and Takeuchi, H and Mima, T},
title = {Brain-Computer Interface Training Based on Brain Activity Can Induce Motor Recovery in Patients With Stroke: A Meta-Analysis.},
journal = {Neurorehabilitation and neural repair},
volume = {36},
number = {2},
pages = {83-96},
doi = {10.1177/15459683211062895},
pmid = {34958261},
issn = {1552-6844},
mesh = {*Brain-Computer Interfaces ; Humans ; Motor Activity/*physiology ; Recovery of Function/*physiology ; Stroke/physiopathology/*therapy ; *Stroke Rehabilitation ; Upper Extremity/*physiopathology ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) is a procedure involving brain activity in which neural status is provided to the participants for self-regulation. The current review aims to evaluate the effect sizes of clinical studies investigating the use of BCI-based rehabilitation interventions in restoring upper extremity function and effective methods to detect brain activity for motor recovery.<br><br>METHODS: A computerized search of MEDLINE, CENTRAL, Web of Science, and PEDro was performed to identify relevant articles. We selected clinical trials that used BCI-based training for post-stroke patients and provided motor assessment scores before and after the intervention. The pooled standardized mean differences of BCI-based training were calculated using the random-effects model.<br><br>RESULTS: We initially identified 655 potentially relevant articles; finally, 16 articles fulfilled the inclusion criteria, involving 382 participants. A significant effect of neurofeedback intervention for the paretic upper limb was observed (standardized mean difference = .48, [.16-.80], P = .006). However, the effect estimates were moderately heterogeneous among the studies (I2 = 45%, P = .03). Subgroup analysis of the method of measurement of brain activity indicated the effectiveness of the algorithm focusing on sensorimotor rhythm.<br><br>CONCLUSION: This meta-analysis suggested that BCI-based training was superior to conventional interventions for motor recovery of the upper limbs in patients with stroke. However, the results are not conclusive because of a high risk of bias and a large degree of heterogeneity due to the differences in the BCI interventions and the participants; therefore, further studies involving larger cohorts are required to confirm these results.},
}
@article {pmid34958014,
year = {2022},
author = {Massetti, N and Russo, M and Franciotti, R and Nardini, D and Mandolini, GM and Granzotto, A and Bomba, M and Delli Pizzi, S and Mosca, A and Scherer, R and Onofrj, M and Sensi, SL and , and , },
title = {A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer's Disease Spectrum.},
journal = {Journal of Alzheimer's disease : JAD},
volume = {85},
number = {4},
pages = {1639-1655},
doi = {10.3233/JAD-210573},
pmid = {34958014},
issn = {1875-8908},
support = {U01 AG024904/AG/NIA NIH HHS/United States ; //CIHR/Canada ; R01 AG046171/AG/NIA NIH HHS/United States ; RF1 AG051550/AG/NIA NIH HHS/United States ; },
mesh = {Aged ; Algorithms ; Alzheimer Disease/*diagnosis ; Biomarkers/cerebrospinal fluid ; Brain/pathology ; Cognitive Dysfunction/diagnosis ; Databases, Factual ; *Disease Progression ; Female ; Humans ; *Machine Learning ; Magnetic Resonance Imaging ; Male ; Neuropsychological Tests ; },
abstract = {BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict the conversion from MCI to AD.<br><br>OBJECTIVE: To evaluate the use of machine learning (ML) on a wealth of data offered by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Alzheimer's Disease Metabolomics Consortium (ADMC) database in the prediction of the MCI to AD conversion.<br><br>METHODS: We implemented an ML-based Random Forest (RF) algorithm to predict conversion from MCI to AD. Data related to the study population (587 MCI subjects) were analyzed by RF as separate or combined features and assessed for classification power. Four classes of variables were considered: neuropsychological test scores, AD-related cerebrospinal fluid (CSF) biomarkers, peripheral biomarkers, and structural magnetic resonance imaging (MRI) variables.<br><br>RESULTS: The ML-based algorithm exhibited 86% accuracy in predicting the AD conversion of MCI subjects. When assessing the features that helped the most, neuropsychological test scores, MRI data, and CSF biomarkers were the most relevant in the MCI to AD prediction. Peripheral parameters were effective when employed in association with neuropsychological test scores. Age and sex differences modulated the prediction accuracy. AD conversion was more effectively predicted in females and younger subjects.<br><br>CONCLUSION: Our findings support the notion that AD-related neurodegenerative processes result from the concerted activity of multiple pathological mechanisms and factors that act inside and outside the brain and are dynamically affected by age and sex.},
}
@article {pmid34956344,
year = {2021},
author = {Rui, Z and Gu, Z},
title = {A Review of EEG and fMRI Measuring Aesthetic Processing in Visual User Experience Research.},
journal = {Computational intelligence and neuroscience},
volume = {2021},
number = {},
pages = {2070209},
pmid = {34956344},
issn = {1687-5273},
mesh = {*Brain/diagnostic imaging ; Electroencephalography ; Esthetics ; Humans ; *Magnetic Resonance Imaging ; User-Computer Interface ; },
abstract = {In human-computer interaction, the visual interaction of user experience (UX) and user interface (UI) plays an important role in enriching the quality of daily life. The purpose of our study analyzes the use of brain-computer interface (BCI), wearable technology, and functional magnetic resonance imaging (fMRI) to explore the aesthetic processing of visual neural response to UI and UX designs. Specifically, this review aims to understand neuroaesthetic processing knowledge, aesthetic appreciation models, and the ways in which visual brain studies can improve the quality of current and future UI and UX designs. Recent research has found that subjective evaluations of aesthetic appreciation produce different results for objective evaluations of brain research analysis. We applied SWOT analysis and examined the advantages and disadvantages of both evaluation methods. Furthermore, we conducted a traditional literature review on topics pertaining to the use of aesthetic processing knowledge in the visual interaction field in terms of art therapy, information visualization, website or mobile applications, and other interactive platforms. Our main research findings from current studies have helped and motivated researchers and designers to use convincing scientific knowledge of brain event-related potential, electroencephalography, and fMRI to understand aesthetic judgment. The key trend finds that many designers, artists, and engineers use artistic BCI technology in the visual interaction experience. Herein, the scientific methods applied in the aesthetic appreciation to human-computer interface are summarized, and the influence of the latest wearable brain technology on visual interaction design is discussed. Furthermore, current possible research entry points for aesthetics, usability, and creativity in UI and UX designs are explicated. The study results have implications for the visual user experience research domain as well as for interaction industries, which produce interactive projects to improve people's daily lives.},
}
@article {pmid34954026,
year = {2022},
author = {Mirjalili, S and Powell, P and Strunk, J and James, T and Duarte, A},
title = {Evaluation of classification approaches for distinguishing brain states predictive of episodic memory performance from electroencephalography: Abbreviated Title: Evaluating methods of classifying memory states from EEG.},
journal = {NeuroImage},
volume = {247},
number = {},
pages = {118851},
pmid = {34954026},
issn = {1095-9572},
support = {T32 AG000175/AG/NIA NIH HHS/United States ; },
mesh = {Adult ; Aged ; Bayes Theorem ; Brain-Computer Interfaces ; Datasets as Topic ; Electroencephalography/*methods ; Female ; Humans ; Male ; *Memory, Episodic ; Mental Recall ; Middle Aged ; },
abstract = {Previous studies have attempted to separate single trial neural responses for events a person is likely to remember from those they are likely to forget using machine learning classification methods. Successful single trial classification holds potential for translation into the clinical realm for real-time detection of memory and other cognitive states to provide real-time interventions (i.e., brain-computer interfaces). However, most of these studies-and classification analyses in general- do not make clear if the chosen methodology is optimally suited for the classification of memory-related brain states. To address this problem, we systematically compared different methods for every step of classification (i.e., feature extraction, feature selection, classifier selection) to investigate which methods work best for decoding episodic memory brain states-the first analysis of its kind. Using an adult lifespan sample EEG dataset collected during performance of an episodic context encoding and retrieval task, we found that no specific feature type (including Common Spatial Pattern (CSP)-based features, mean, variance, correlation, features based on AR model, entropy, phase, and phase synchronization) outperformed others consistently in distinguishing different memory classes. However, extracting all of these feature types consistently outperformed extracting only one type of feature. Additionally, the combination of filtering and sequential forward selection was the optimal method to select the effective features compared to filtering alone or performing no feature selection at all. Moreover, although all classifiers performed at a fairly similar level, LASSO was consistently the highest performing classifier compared to other commonly used options (i.e., naïve Bayes, SVM, and logistic regression) while naïve Bayes was the fastest classifier. Lastly, for multiclass classification (i.e., levels of context memory confidence and context feature perception), generalizing the binary classification using the binary decision tree performed better than the voting or one versus rest method. These methods were shown to outperform alternative approaches for three orthogonal datasets (i.e., EEG working memory, EEG motor imagery, and MEG working memory), supporting their generalizability. Our results provide an optimized methodological process for classifying single-trial neural data and provide important insight and recommendations for a cognitive neuroscientist's ability to make informed choices at all stages of the classification process for predicting memory and other cognitive states.},
}
@article {pmid34952226,
year = {2022},
author = {Cheng, Z and Wang, C and Wei, B and Gan, W and Zhou, Q and Cui, M},
title = {High resolution ultrasonic neural modulation observed via in vivo two-photon calcium imaging.},
journal = {Brain stimulation},
volume = {15},
number = {1},
pages = {190-196},
doi = {10.1016/j.brs.2021.12.005},
pmid = {34952226},
issn = {1876-4754},
support = {R01 NS118330/NS/NINDS NIH HHS/United States ; U01 NS094341/NS/NINDS NIH HHS/United States ; UF1 NS107689/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain/diagnostic imaging/physiology ; *Calcium ; Mammals ; Mice ; Neurons/physiology ; *Ultrasonics ; Ultrasonography/methods ; },
abstract = {Neural modulation plays a major role in delineating the circuit mechanisms and serves as the cornerstone of neural interface technologies. Among the various modulation mechanisms, ultrasound enables noninvasive label-free deep access to mammalian brain tissue. To date, most if not all ultrasonic neural modulation implementations are based on ∼1 MHz carrier frequency. The long acoustic wavelength results in a spatially coarse modulation zone, often spanning over multiple function regions. The modulation of one function region is inevitably linked with the modulation of its neighboring regions. Moreover, the lack of in vivo cellular resolution cell-type-specific recording capabilities in most studies prevents the revealing of the genuine cellular response to ultrasound. To significantly increase the spatial resolution, we explored the application of high-frequency ultrasound. To investigate the neuronal response at cellular resolutions, we developed a dual-modality system combining in vivo two-photon calcium imaging and focused ultrasound modulation. The studies show that the ∼30 MHz ultrasound can suppress the neuronal activity in awake mice at 100-μm scale spatial resolutions, paving the way for high-resolution ultrasonic neural modulation. The dual-modality in vivo system validated through this study will serve as a general platform for studying the dynamics of various cell types in response to ultrasound.},
}
@article {pmid34951857,
year = {2021},
author = {Wang, K and Zhai, DH and Xiong, Y and Hu, L and Xia, Y},
title = {An MVMD-CCA Recognition Algorithm in SSVEP-Based BCI and Its Application in Robot Control.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2021.3135696},
pmid = {34951857},
issn = {2162-2388},
abstract = {This article proposes a novel recognition algorithm for the steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) system. By combining the advantages of multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA), an MVMD-CCA algorithm is investigated to improve the detection ability of SSVEP electroencephalogram (EEG) signals. In comparison with the classical filter bank canonical correlation analysis (FBCCA), the nonlinear and non-stationary EEG signals are decomposed into a fixed number of sub-bands by MVMD, which can enhance the effect of SSVEP-related sub-bands. The experimental results show that MVMD-CCA can effectively reduce the influence of noise and EEG artifacts and improve the performance of SSVEP-based BCI. The offline experiments show that the average accuracies of MVMD-CCA in the training dataset and testing dataset are improved by 3.08% and 1.67%, respectively. In the SSVEP-based online robotic manipulator grasping experiment, the recognition accuracies of the four subjects are 92.5%, 93.33%, 90.83%, and 91.67%, respectively.},
}
@article {pmid34950640,
year = {2021},
author = {Woeppel, K and Hughes, C and Herrera, AJ and Eles, JR and Tyler-Kabara, EC and Gaunt, RA and Collinger, JL and Cui, XT},
title = {Explant Analysis of Utah Electrode Arrays Implanted in Human Cortex for Brain-Computer-Interfaces.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {9},
number = {},
pages = {759711},
pmid = {34950640},
issn = {2296-4185},
abstract = {Brain-computer interfaces are being developed to restore movement for people living with paralysis due to injury or disease. Although the therapeutic potential is great, long-term stability of the interface is critical for widespread clinical implementation. While many factors can affect recording and stimulation performance including electrode material stability and host tissue reaction, these factors have not been investigated in human implants. In this clinical study, we sought to characterize the material integrity and biological tissue encapsulation via explant analysis in an effort to identify factors that influence electrophysiological performance. We examined a total of six Utah arrays explanted from two human participants involved in intracortical BCI studies. Two platinum (Pt) arrays were implanted for 980 days in one participant (P1) and two Pt and two iridium oxide (IrOx) arrays were implanted for 182 days in the second participant (P2). We observed that the recording quality followed a similar trend in all six arrays with an initial increase in peak-to-peak voltage during the first 30-40 days and gradual decline thereafter in P1. Using optical and two-photon microscopy we observed a higher degree of tissue encapsulation on both arrays implanted for longer durations in participant P1. We then used scanning electron microscopy and energy dispersive X-ray spectroscopy to assess material degradation. All measures of material degradation for the Pt arrays were found to be more prominent in the participant with a longer implantation time. Two IrOx arrays were subjected to brief survey stimulations, and one of these arrays showed loss of iridium from most of the stimulated sites. Recording performance appeared to be unaffected by this loss of iridium, suggesting that the adhesion of IrOx coating may have been compromised by the stimulation, but the metal layer did not detach until or after array removal. In summary, both tissue encapsulation and material degradation were more pronounced in the arrays that were implanted for a longer duration. Additionally, these arrays also had lower signal amplitude and impedance. New biomaterial strategies that minimize fibrotic encapsulation and enhance material stability should be developed to achieve high quality recording and stimulation for longer implantation periods.},
}
@article {pmid34949983,
year = {2021},
author = {Chen, H and Jin, M and Li, Z and Fan, C and Li, J and He, H},
title = {MS-MDA: Multisource Marginal Distribution Adaptation for Cross-Subject and Cross-Session EEG Emotion Recognition.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {778488},
pmid = {34949983},
issn = {1662-4548},
abstract = {As an essential element for the diagnosis and rehabilitation of psychiatric disorders, the electroencephalogram (EEG) based emotion recognition has achieved significant progress due to its high precision and reliability. However, one obstacle to practicality lies in the variability between subjects and sessions. Although several studies have adopted domain adaptation (DA) approaches to tackle this problem, most of them treat multiple EEG data from different subjects and sessions together as a single source domain for transfer, which either fails to satisfy the assumption of domain adaptation that the source has a certain marginal distribution, or increases the difficulty of adaptation. We therefore propose the multi-source marginal distribution adaptation (MS-MDA) for EEG emotion recognition, which takes both domain-invariant and domain-specific features into consideration. First, we assume that different EEG data share the same low-level features, then we construct independent branches for multiple EEG data source domains to adopt one-to-one domain adaptation and extract domain-specific features. Finally, the inference is made by multiple branches. We evaluate our method on SEED and SEED-IV for recognizing three and four emotions, respectively. Experimental results show that the MS-MDA outperforms the comparison methods and state-of-the-art models in cross-session and cross-subject transfer scenarios in our settings. Codes at https://github.com/VoiceBeer/MS-MDA.},
}
@article {pmid34949982,
year = {2021},
author = {Arlotti, M and Colombo, M and Bonfanti, A and Mandat, T and Lanotte, MM and Pirola, E and Borellini, L and Rampini, P and Eleopra, R and Rinaldo, S and Romito, L and Janssen, MLF and Priori, A and Marceglia, S},
title = {A New Implantable Closed-Loop Clinical Neural Interface: First Application in Parkinson's Disease.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {763235},
pmid = {34949982},
issn = {1662-4548},
abstract = {Deep brain stimulation (DBS) is used for the treatment of movement disorders, including Parkinson's disease, dystonia, and essential tremor, and has shown clinical benefits in other brain disorders. A natural path for the improvement of this technique is to continuously observe the stimulation effects on patient symptoms and neurophysiological markers. This requires the evolution of conventional deep brain stimulators to bidirectional interfaces, able to record, process, store, and wirelessly communicate neural signals in a robust and reliable fashion. Here, we present the architecture, design, and first use of an implantable stimulation and sensing interface (AlphaDBSR System) characterized by artifact-free recording and distributed data management protocols. Its application in three patients with Parkinson's disease (clinical trial n. NCT04681534) is shown as a proof of functioning of a clinically viable implanted brain-computer interface (BCI) for adaptive DBS. Reliable artifact free-recordings, and chronic long-term data and neural signal management are in place.},
}
@article {pmid34945880,
year = {2021},
author = {Marcos-Martínez, D and Martínez-Cagigal, V and Santamaría-Vázquez, E and Pérez-Velasco, S and Hornero, R},
title = {Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population.},
journal = {Entropy (Basel, Switzerland)},
volume = {23},
number = {12},
pages = {},
pmid = {34945880},
issn = {1099-4300},
support = {RTC2019-007350-1//Ministerio de Ciencia, Innovación y Universidades de España/ ; 0702_MIGRAINEE_2_E//European Comission/ ; },
abstract = {Neurofeedback training (NFT) has shown promising results in recent years as a tool to address the effects of age-related cognitive decline in the elderly. Since previous studies have linked reduced complexity of electroencephalography (EEG) signal to the process of cognitive decline, we propose the use of non-linear methods to characterise changes in EEG complexity induced by NFT. In this study, we analyse the pre- and post-training EEG from 11 elderly subjects who performed an NFT based on motor imagery (MI-NFT). Spectral changes were studied using relative power (RP) from classical frequency bands (delta, theta, alpha, and beta), whilst multiscale entropy (MSE) was applied to assess EEG-induced complexity changes. Furthermore, we analysed the subject's scores from Luria tests performed before and after MI-NFT. We found that MI-NFT induced a power shift towards rapid frequencies, as well as an increase of EEG complexity in all channels, except for C3. These improvements were most evident in frontal channels. Moreover, results from cognitive tests showed significant enhancement in intellectual and memory functions. Therefore, our findings suggest the usefulness of MI-NFT to improve cognitive functions in the elderly and encourage future studies to use MSE as a metric to characterise EEG changes induced by MI-NFT.},
}
@article {pmid34945371,
year = {2021},
author = {Yuan, H and Li, Y and Yang, J and Li, H and Yang, Q and Guo, C and Zhu, S and Shu, X},
title = {State of the Art of Non-Invasive Electrode Materials for Brain-Computer Interface.},
journal = {Micromachines},
volume = {12},
number = {12},
pages = {},
pmid = {34945371},
issn = {2072-666X},
support = {51672173//National Natural Science Foundation of China/ ; 51801121//National Natural Science Foundation of China/ ; 51572169//National Natural Science Foundation of China/ ; 51902200//National Natural Science Foundation of China/ ; 52005321//National Natural Science Foundation of China/ ; 2017YFE0113000//Key Program for International S&amp;T Cooperation Program of China/ ; 17JC1400700//Science and Technology Commission of Shanghai Municipality/ ; 18JC1410500//Science and Technology Commission of Shanghai Municipality/ ; 18ZR1421000//Science and Technology Commission of Shanghai Municipality/ ; 18520744700//Science and Technology Commission of Shanghai Municipality/ ; 19ZR1425300//Science and Technology Commission of Shanghai Municipality/ ; 2016A010103018//Science and Technology Planning Project of Guangdong Province/ ; 2016XCWZK15//Shanghai Research Institute of Criminal Science and Technology/ ; YG2017QN11//Medical-Engineering Cross Research Funding of SJTU/ ; 2016YFA0202900//National Key R&amp;D Program of China/ ; },
abstract = {The brain-computer interface (BCI) has emerged in recent years and has attracted great attention. As an indispensable part of the BCI signal acquisition system, brain electrodes have a great influence on the quality of the signal, which determines the final effect. Due to the special usage scenario of brain electrodes, some specific properties are required for them. In this study, we review the development of three major types of EEG electrodes from the perspective of material selection and structural design, including dry electrodes, wet electrodes, and semi-dry electrodes. Additionally, we provide a reference for the current chaotic performance evaluation of EEG electrodes in some aspects such as electrochemical performance, stability, and so on. Moreover, the challenges and future expectations for EEG electrodes are analyzed.},
}
@article {pmid34945296,
year = {2021},
author = {Kim, Y and Ereifej, ES and Schwartzman, WE and Meade, SM and Chen, K and Rayyan, J and Feng, H and Aluri, V and Mueller, NN and Bhambra, R and Bhambra, S and Taylor, DM and Capadona, JR},
title = {Investigation of the Feasibility of Ventricular Delivery of Resveratrol to the Microelectrode Tissue Interface.},
journal = {Micromachines},
volume = {12},
number = {12},
pages = {},
pmid = {34945296},
issn = {2072-666X},
support = {RX002628-01A1//United States Department of Veterans Affairs/ ; GRANT12418820//United States Department of Veterans Affairs/ ; GRANT12647351//United States Department of Veterans Affairs/ ; GRANT12635707//United States Department of Veterans Affairs/ ; GRANT12635723/NS/NINDS NIH HHS/United States ; T32EB004314//National Institute for Biomedical Imaging and Bioengineering/ ; },
abstract = {(1) Background: Intracortical microelectrodes (IMEs) are essential to basic brain research and clinical brain-machine interfacing applications. However, the foreign body response to IMEs results in chronic inflammation and an increase in levels of reactive oxygen and nitrogen species (ROS/RNS). The current study builds on our previous work, by testing a new delivery method of a promising antioxidant as a means of extending intracortical microelectrodes performance. While resveratrol has shown efficacy in improving tissue response, chronic delivery has proven difficult because of its low solubility in water and low bioavailability due to extensive first pass metabolism. (2) Methods: Investigation of an intraventricular delivery of resveratrol in rats was performed herein to circumvent bioavailability hurdles of resveratrol delivery to the brain. (3) Results: Intraventricular delivery of resveratrol in rats delivered resveratrol to the electrode interface. However, intraventricular delivery did not have a significant impact on electrophysiological recordings over the six-week study. Histological findings indicated that rats receiving intraventricular delivery of resveratrol had a decrease of oxidative stress, yet other biomarkers of inflammation were found to be not significantly different from control groups. However, investigation of the bioavailability of resveratrol indicated a decrease in resveratrol accumulation in the brain with time coupled with inconsistent drug elution from the cannulas. Further inspection showed that there may be tissue or cellular debris clogging the cannulas, resulting in variable elution, which may have impacted the results of the study. (4) Conclusions: These results indicate that the intraventricular delivery approach described herein needs further optimization, or may not be well suited for this application.},
}
@article {pmid34945210,
year = {2021},
author = {Cywka, KB and Skarżyński, H and Król, B and Skarżyński, PH},
title = {The Bonebridge BCI 602 Active Transcutaneous Bone Conduction Implant in Children: Objective and Subjective Benefits.},
journal = {Journal of clinical medicine},
volume = {10},
number = {24},
pages = {},
pmid = {34945210},
issn = {2077-0383},
abstract = {BACKGROUND: the Bonebridge hearing implant is an active transcutaneous bone conduction implant suitable for various types of hearing loss. It was first launched in 2012 as the BCI 601, with a newer internal part (BCI 602) released in 2019. With the new size and shape, the BCI 602 can be used in patients previously excluded due to insufficient anatomical conditions, especially in patients with congenital defects of the outer and middle ear.<br><br>OBJECTIVES: the purpose of this study is to evaluate the objective and subjective benefits of the new Bonebridge BCI 602 in children who have hearing impairment due to conductive or mixed hearing loss. Safety and effectiveness of the device was assessed.<br><br>METHODS: the study group included 22 children aged 8-18 years (mean age 14.7 years) who had either conductive or mixed hearing loss. All patients were implanted unilaterally with the new Bonebridge BCI 602 implant. Pure tone audiometry, speech recognition tests (in quiet and noise), and free-field audiometry were performed before and after implantation. Word recognition scores were evaluated using the Demenko and Pruszewicz Polish Monosyllabic Word Test, and speech reception thresholds in noise were assessed using the Polish Sentence Matrix Test. The subjective assessment of benefits was carried outusing the APHAB (Abbreviated Profile of Hearing Aid Benefit) questionnaire.<br><br>RESULTS: after implantation of the Bonebridge BCI 602 all patients showed a statistically significant improvement in hearing and speech understanding. The mean word recognition score (WRS) changed from 12.1% before implantation to 87.3% after 6 months. Mean speech reception threshold (SRT) before implantation was +4.79 dB SNR and improved to -1.29 dB SNR after 6 months. All patients showed stable postoperative results. The APHAB questionnaire showed that difficulties in hearing decreased after implantation, with a statistically significant improvement in global score. Pre-operative scores (M = 35.7) were significantly worse than post-operative scores at 6 months (M = 25.7).<br><br>CONCLUSIONS: the present study confirms that the Bonebridge BCI 602 is an innovative and effective solution, especially for patients with conductive and mixed hearing loss due to anatomical ear defects. The Bonebridge BCI 602 system provides valuable and stable audiological and surgical benefits. Subjective assessment also confirms the effectiveness of the BCI 602. The BCI 602 offers the same amplification as the BCI601, but with a smaller size. The smaller dimensions make it an effective treatment option for a wider group of patients, especially children with congenital defects of the outer and middle ear.},
}
@article {pmid34942608,
year = {2022},
author = {Niu, X and Lu, N and Kang, J and Cui, Z},
title = {Knowledge-driven feature component interpretable network for motor imagery classification.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac463a},
pmid = {34942608},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination/physiology ; Neural Networks, Computer ; },
abstract = {Objective. The end-to-end convolutional neural network (CNN) has achieved great success in motor imagery (MI) classification without a manual feature design. However, all the existing deep network solutions are purely datadriven and lack interpretability, which makes it impossible to discover insightful knowledge from the learned features, not to mention to design specific network structures. The heavy computational cost of CNN also makes it challenging for real-time application along with high classification performance.Approach. To address these problems, a novel knowledge-driven feature component interpretable network (KFCNet) is proposed, which combines spatial and temporal convolution in analogy to independent component analysis and a power spectrum pipeline. Prior frequency band knowledge of sensory-motor rhythms has been formulated as band-pass linear-phase digital finite impulse response filters to initialize the temporal convolution kernels to enable the knowledge-driven mechanism. To avoid signal distortion and achieve a linear phase and unimodality of filters, a symmetry loss is proposed, which is used in combination with the cross-entropy classification loss for training. Besides the general prior knowledge, subject-specific time-frequency property of event-related desynchronization and synchronization has been employed to construct and initialize the network with significantly fewer parameters.Main results.Comparison of experiments on two public datasets has been performed. Interpretable feature components could be observed in the trained model. The physically meaningful observation could efficiently assist the design of the network structure. Excellent classification performance on MI has been obtained.Significance. The performance of KFCNet is comparable to the state-of-the-art methods but with much fewer parameters and makes real-time applications possible.},
}
@article {pmid34939217,
year = {2022},
author = {Zabcikova, M and Koudelkova, Z and Jasek, R and Lorenzo Navarro, JJ},
title = {Recent advances and current trends in brain-computer interface research and their applications.},
journal = {International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience},
volume = {82},
number = {2},
pages = {107-123},
doi = {10.1002/jdn.10166},
pmid = {34939217},
issn = {1873-474X},
support = {IGA/CebiaTech/2021/005//Internal Grant Agency/ ; },
mesh = {Brain ; *Brain-Computer Interfaces ; *Communication Aids for Disabled ; Electroencephalography ; Humans ; User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) provides direct communication between the brain and an external device. BCI systems have become a trendy field of research in recent years. These systems can be used in a variety of applications to help both disabled and healthy people. Concerning significant BCI progress, we may assume that these systems are not very far from real-world applications. This review has taken into account current trends in BCI research. In this survey, 100 most cited articles from the WOS database were selected over the last 4 years. This survey is divided into several sectors. These sectors are Medicine, Communication and Control, Entertainment, and Other BCI applications. The application area, recording method, signal acquisition types, and countries of origin have been identified in each article. This survey provides an overview of the BCI articles published from 2016 to 2020 and their current trends and advances in different application areas.},
}
@article {pmid34937017,
year = {2022},
author = {Lapborisuth, P and Koorathota, S and Wang, Q and Sajda, P},
title = {Integrating neural and ocular attention reorienting signals in virtual reality.},
journal = {Journal of neural engineering},
volume = {18},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac4593},
pmid = {34937017},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; Eye ; Fixation, Ocular ; Humans ; *Virtual Reality ; },
abstract = {Objective.Reorienting is central to how humans direct attention to different stimuli in their environment. Previous studies typically employ well-controlled paradigms with limited eye and head movements to study the neural and physiological processes underlying attention reorienting. Here, we aim to better understand the relationship between gaze and attention reorienting using a naturalistic virtual reality (VR)-based target detection paradigm.Approach.Subjects were navigated through a city and instructed to count the number of targets that appeared on the street. Subjects performed the task in a fixed condition with no head movement and in a free condition where head movements were allowed. Electroencephalography (EEG), gaze and pupil data were collected. To investigate how neural and physiological reorienting signals are distributed across different gaze events, we used hierarchical discriminant component analysis (HDCA) to identify EEG and pupil-based discriminating components. Mixed-effects general linear models (GLM) were used to determine the correlation between these discriminating components and the different gaze events time. HDCA was also used to combine EEG, pupil and dwell time signals to classify reorienting events.Main results.In both EEG and pupil, dwell time contributes most significantly to the reorienting signals. However, when dwell times were orthogonalized against other gaze events, the distributions of the reorienting signals were different across the two modalities, with EEG reorienting signals leading that of the pupil reorienting signals. We also found that the hybrid classifier that integrates EEG, pupil and dwell time features detects the reorienting signals in both the fixed (AUC = 0.79) and the free (AUC = 0.77) condition.Significance.We show that the neural and ocular reorienting signals are distributed differently across gaze events when a subject is immersed in VR, but nevertheless can be captured and integrated to classify target vs. distractor objects to which the human subject orients.},
}
@article {pmid34932486,
year = {2021},
author = {Wang, X and Cavigelli, L and Schneider, T and Benini, L},
title = {Sub-100 μW Multispectral Riemannian Classification for EEG-Based Brain-Machine Interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {15},
number = {6},
pages = {1149-1160},
doi = {10.1109/TBCAS.2021.3137290},
pmid = {34932486},
issn = {1940-9990},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Neural Networks, Computer ; },
abstract = {Motor imagery (MI) brain-machine interfaces (BMIs) enable us to control machines by merely thinking of performing a motor action. Practical use cases require a wearable solution where the classification of the brain signals is done locally near the sensor using machine learning models embedded on energy-efficient microcontroller units (MCUs), for assured privacy, user comfort, and long-term usage. In this work, we provide practical insights on the accuracy-cost trade-off for embedded BMI solutions. Our multispectral Riemannian classifier reaches 75.1% accuracy on a 4-class MI task. The accuracy is further improved by tuning different types of classifiers to each subject, achieving 76.4%. We further scale down the model by quantizing it to mixed-precision representations with a minimal accuracy loss of 1% and 1.4%, respectively, which is still up to 4.1% more accurate than the state-of-the-art embedded convolutional neural network. We implement the model on a low-power MCU within an energy budget of merely 198 μJ and taking only 16.9 ms per classification. Classifying samples continuously, overlapping the 3.5 s samples by 50% to avoid missing user inputs allows for operation at just 85 μW. Compared to related works in embedded MI-BMIs, our solution sets the new state-of-the-art in terms of accuracy-energy trade-off for near-sensor classification.},
}
@article {pmid34932480,
year = {2021},
author = {Wang, P and Zhou, Y and Li, Z and Huang, S and Zhang, D},
title = {Neural Decoding of Chinese Sign Language With Machine Learning for Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {2721-2732},
doi = {10.1109/TNSRE.2021.3137340},
pmid = {34932480},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; China ; Electroencephalography ; Humans ; Imagination ; Machine Learning ; Movement ; Sign Language ; },
abstract = {Limb motion decoding is an important part of brain-computer interface (BCI) research. Among the limb motion, sign language not only contains rich semantic information and abundant maneuverable actions but also provides different executable commands. However, many researchers focus on decoding the gross motor skills, such as the decoding of ordinary motor imagery or simple upper limb movements. Here we explored the neural features and decoding of Chinese sign language from electroencephalograph (EEG) signal with motor imagery and motor execution. Sign language not only contains rich semantic information, but also has abundant maneuverable actions, and provides us with more different executable commands. In this paper, twenty subjects were instructed to perform movement execution and movement imagery based on Chinese sign language. Seven classifiers are employed to classify the selected features of sign language EEG. L1 regularization is used to learn and select features that contain more information from the mean, power spectral density, sample entropy, and brain network connectivity. The best average classification accuracy of the classifier is 89.90% (imagery sign language is 83.40%). These results have shown the feasibility of decoding between different sign languages. The source location reveals that the neural circuits involved in sign language are related to the visual contact area and the pre-movement area. Experimental evaluation shows that the proposed decoding strategy based on sign language can obtain outstanding classification results, which provides a certain reference value for the subsequent research of limb decoding based on sign language.},
}
@article {pmid34932469,
year = {2021},
author = {Autthasan, P and Chaisaen, R and Sudhawiyangkul, T and Kiatthaveephong, S and Rangpong, P and Dilokthanakul, N and Bhakdisongkhram, G and Phan, H and Guan, C and Wilaiprasitporn, T},
title = {MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2021.3137184},
pmid = {34932469},
issn = {1558-2531},
abstract = {OBJECTIVE: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded by electroencephalography (EEG) using a non-invasive technique. Despite significant advances in MI-based BCI, EEG rhythms are specific to a subject and various changes over time. These issues point to significant challenges to enhance the classification performance, especially in a subject-independent manner.<br><br>METHODS: To overcome these challenges, we propose MIN2Net, a novel end-to-end multi-task learning to tackle this task. We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification simultaneously.<br><br>RESULTS: This approach reduces the complexity in pre-processing, results in significant performance improvement on EEG classification. Experimental results in a subject-independent manner show that MIN2Net outperforms the state-of-the-art techniques, achieving an F1-score improvement of 6.72 %, and 2.23 % on the SMR-BCI, and OpenBMI datasets, respectively.<br><br>CONCLUSION: We demonstrate that MIN2Net improves discriminative information in the latent representation.<br><br>SIGNIFICANCE: This study indicates the possibility and practicality of using this model to develop MI-based BCI applications for new users without calibration.},
}
@article {pmid34932468,
year = {2022},
author = {Libert, A and Wittevrongel, B and Camarrone, F and Van Hulle, MM},
title = {Phase-Spatial Beamforming Renders a Visual Brain Computer Interface Capable of Exploiting EEG Electrode Phase Shifts in Motion-Onset Target Responses.},
journal = {IEEE transactions on bio-medical engineering},
volume = {69},
number = {5},
pages = {1802-1812},
doi = {10.1109/TBME.2021.3136938},
pmid = {34932468},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; Electrodes ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Motion ; Photic Stimulation ; },
abstract = {OBJECTIVE: in this work, we aim to develop a more efficient visual motion-onset based Brain-computer interface (BCI). Brain-computer interfaces provide communication facilities that do not rely on the brain's usual pathways. Visual BCIs are based on changes in EEG activity in response to attended flashing or flickering targets. A less taxing way to encode such targets is with briefly moving stimuli, the onset of which elicits a lateralized EEG potential over the parieto-occipital scalp area called the motion-onset visual evoked potential (mVEP).<br><br>METHODS: We recruited 21 healthy subjects for an experiment in which motion-onset stimulations translating leftwards (LT) or rightwards (RT) were encoding 9 displayed targets. We propose a novel algorithm that exploits the phase-shift between EEG electrodes to improve target decoding performance. We hereto extend the spatiotemporal beamformer (stBF) with a phase extracting procedure, leading to the phase-spatial beamformer (psBF).<br><br>RESULTS: we show that psBF performs significantly better than the stBF (p < 0.001 for 1 and 2 stimulus repetitions and p < 0.01 for 3 to 5 stimulus repetitions), as well as the previously validated linear support-vector machines (p < 0.001 for 5 stimulus repetitions and p < 0.01 for 1,2 and 6 stimulus repetitions) and stepwise linear discriminant analysis decoders (p < 0.001 for all repetitions) when simultaneously addressing timing and translation direction.<br><br>CONCLUSION: We provide evidence of decodability of joint direction and target in mVEP responses.<br><br>SIGNIFICANCE: the described methods can aid in the development of a faster and more comfortable BCI based on mVEPs.},
}
@article {pmid34930955,
year = {2021},
author = {Sherathiya, VN and Schaid, MD and Seiler, JL and Lopez, GC and Lerner, TN},
title = {GuPPy, a Python toolbox for the analysis of fiber photometry data.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {24212},
pmid = {34930955},
issn = {2045-2322},
support = {DP2 MH122401/MH/NIMH NIH HHS/United States ; P50 DA044121/DA/NIDA NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Area Under Curve ; Artifacts ; Brain/diagnostic imaging ; Calcium/chemistry ; Computer Graphics ; Dopamine/chemistry ; Female ; Image Processing, Computer-Assisted/*methods ; Male ; Mice ; Mice, Inbred C57BL ; Mice, Transgenic ; Neuroimaging/*instrumentation/*methods ; Neurosciences ; Photometry/*instrumentation/*methods ; Programming Languages ; *Software ; Stereotaxic Techniques ; User-Computer Interface ; },
abstract = {Fiber photometry (FP) is an adaptable method for recording in vivo neural activity in freely behaving animals. It has become a popular tool in neuroscience due to its ease of use, low cost, the ability to combine FP with freely moving behavior, among other advantages. However, analysis of FP data can be challenging for new users, especially those with a limited programming background. Here, we present Guided Photometry Analysis in Python (GuPPy), a free and open-source FP analysis tool. GuPPy is designed to operate across computing platforms and can accept data from a variety of FP data acquisition systems. The program presents users with a set of graphic user interfaces (GUIs) to load data and provide input parameters. Graphs are produced that can be easily exported for integration into scientific figures. As an open-source tool, GuPPy can be modified by users with knowledge of Python to fit their specific needs.},
}
@article {pmid34930915,
year = {2021},
author = {Lee, YE and Shin, GH and Lee, M and Lee, SW},
title = {Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running.},
journal = {Scientific data},
volume = {8},
number = {1},
pages = {315},
pmid = {34930915},
issn = {2052-4463},
support = {2017-0-00451//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2015-0-00185//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2019-0-00079//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2017-0-00451//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2015-0-00185//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2019-0-00079//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2017-0-00451//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2015-0-00185//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2019-0-00079//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2017-0-00451//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2015-0-00185//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2019-0-00079//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Ear ; Electroencephalography ; *Evoked Potentials ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Running/*physiology ; Scalp ; *Standing Position ; Walking/*physiology ; Young Adult ; },
abstract = {We present a mobile dataset obtained from electroencephalography (EEG) of the scalp and around the ear as well as from locomotion sensors by 24 participants moving at four different speeds while performing two brain-computer interface (BCI) tasks. The data were collected from 32-channel scalp-EEG, 14-channel ear-EEG, 4-channel electrooculography, and 9-channel inertial measurement units placed at the forehead, left ankle, and right ankle. The recording conditions were as follows: standing, slow walking, fast walking, and slight running at speeds of 0, 0.8, 1.6, and 2.0 m/s, respectively. For each speed, two different BCI paradigms, event-related potential and steady-state visual evoked potential, were recorded. To evaluate the signal quality, scalp- and ear-EEG data were qualitatively and quantitatively validated during each speed. We believe that the dataset will facilitate BCIs in diverse mobile environments to analyze brain activities and evaluate the performance quantitatively for expanding the use of practical BCIs.},
}
@article {pmid34925636,
year = {2021},
author = {Schönau, A},
title = {The spectrum of responsibility ascription for end users of neurotechnologies.},
journal = {Neuroethics},
volume = {14},
number = {3},
pages = {423-435},
pmid = {34925636},
issn = {1874-5490},
support = {RF1 MH117800/MH/NIMH NIH HHS/United States ; },
abstract = {Invasive neural devices offer novel prospects for motor rehabilitation on different levels of agentive behavior. From a functional perspective, they interact with, support, or enable human intentional actions in such a way that movement capabilities are regained. However, when there is a technical malfunction resulting in an unintended movement, the complexity of the relationship between the end user and the device sometimes makes it difficult to determine who is responsible for the outcome - a circumstance that has been coined as "responsibility gap" in the literature. So far, recent accounts frame this issue around the theme of control but more work is needed to explore the complicated terrain of assigning responsibility for neural device-mediated actions from this control perspective. This paper aims at contributing to this tendency by offering more fine-grained distinctions of how that control capacity is facilitated by the machine and how it can be exercised by the end user. This results in a novel framework that depicts an in-depth exploration of the control aspect of responsibility in a way that incorporates the diversity of relationships between neurotechnologies, the various conditions they treat, and the individual end user's experience.},
}
@article {pmid34924942,
year = {2021},
author = {Li, S and Duan, J and Sun, Y and Sheng, X and Zhu, X and Meng, J},
title = {Exploring Fatigue Effects on Performance Variation of Intensive Brain-Computer Interface Practice.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {773790},
pmid = {34924942},
issn = {1662-4548},
abstract = {Motor imagery (MI) is an endogenous mental process and is commonly used as an electroencephalogram (EEG)-based brain-computer interface (BCI) strategy. Previous studies of P300 and MI-based (without online feedback) BCI have shown that mental states like fatigue can negatively affect participants' EEG signatures. However, exogenous stimuli cause visual fatigue, which might have a different mechanism than endogenous tasks do. Furthermore, subjects could adjust themselves if online feedback is provided. In this sense, it is still unclear how fatigue affects online MI-based BCI performance. With this question, 12 healthy subjects are recruited to investigate this issue, and an MI-based online BCI experiment is performed for four sessions on different days. The first session is for training, and the other three sessions differ in rest condition and duration-no rest, 16-min eyes-open rest, and 16-min eyes-closed rest-arranged in a pseudo-random order. Multidimensional fatigue inventory (MFI) and short stress state questionnaire (SSSQ) reveal that general fatigue, mental fatigue, and distress have increased, while engagement has decreased significantly within certain sessions. However, the BCI performances, including percent valid correct (PVC) and information transfer rate (ITR), show no significant change across 400 trials. The results suggest that although the repetitive MI task has affected subjects' mental states, their BCI performances and feature separability within a session are not affected by the task significantly. Further electrophysiological analysis reveals that the alpha-band power in the sensorimotor area has an increasing tendency, while event-related desynchronization (ERD) modulation level has a decreasing trend. During the rest time, no physiological difference has been found in the eyes-open rest condition; on the contrary, the alpha-band power increase and subsequent decrease appear in the eyes-closed rest condition. In summary, this experiment shows evidence that mental states can change dramatically in the intensive MI-BCI practice, but BCI performances could be maintained.},
}
@article {pmid34924927,
year = {2021},
author = {Zhang, P and Min, C and Zhang, K and Xue, W and Chen, J},
title = {Hierarchical Spatiotemporal Electroencephalogram Feature Learning and Emotion Recognition With Attention-Based Antagonism Neural Network.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {738167},
pmid = {34924927},
issn = {1662-4548},
abstract = {Inspired by the neuroscience research results that the human brain can produce dynamic responses to different emotions, a new electroencephalogram (EEG)-based human emotion classification model was proposed, named R2G-ST-BiLSTM, which uses a hierarchical neural network model to learn more discriminative spatiotemporal EEG features from local to global brain regions. First, the bidirectional long- and short-term memory (BiLSTM) network is used to obtain the internal spatial relationship of EEG signals on different channels within and between regions of the brain. Considering the different effects of various cerebral regions on emotions, the regional attention mechanism is introduced in the R2G-ST-BiLSTM model to determine the weight of different brain regions, which could enhance or weaken the contribution of each brain area to emotion recognition. Then a hierarchical BiLSTM network is again used to learn the spatiotemporal EEG features from regional to global brain areas, which are then input into an emotion classifier. Especially, we introduce a domain discriminator to work together with the classifier to reduce the domain offset between the training and testing data. Finally, we make experiments on the EEG data of the DEAP and SEED datasets to test and compare the performance of the models. It is proven that our method achieves higher accuracy than those of the state-of-the-art methods. Our method provides a good way to develop affective brain-computer interface applications.},
}
@article {pmid34922702,
year = {2021},
author = {Pimenta, T and Rocha, JA},
title = {Cardiac rehabilitation and improvement of chronotropic incompetence: Is it the exercise or just the beta blockers?.},
journal = {Revista portuguesa de cardiologia},
volume = {40},
number = {12},
pages = {947-953},
doi = {10.1016/j.repce.2021.11.013},
pmid = {34922702},
issn = {2174-2049},
mesh = {Adrenergic beta-Antagonists/therapeutic use ; *Cardiac Rehabilitation ; Exercise Test ; Heart Rate ; Humans ; Retrospective Studies ; },
abstract = {INTRODUCTION: Clinical use of chronotropic response has been limited due to lack of consensus on the appropriate formula for chronotropic index (Ci) calculation and the definition of chronotropic incompetence.<br><br>OBJECTIVES: To assess the effects of cardiac rehabilitation programs (CRP) on Ci, irrespective of betablockers (BB) use and dosage. Assess the relative contribution of change in Ci on improvement in functional capacity.<br><br>METHODS: Retrospective analysis of a sample of patients admitted to a CRP after acute coronary syndrome, with at least 12 months of follow-up. Ci was calculated using the conventional (CCi) and the Brawner formula (BCi) for age-predicted maximum heart rate. Ci and functional capacity were estimated at three time points: T1 and T2, before and at the end of the CRP, and T3, at 12 months. The sample was categorized according to BB dosage modification between T1 and T3: G1 - reduced; G2 - no change; G3 - increased.<br><br>RESULTS: In G1, CCi increased from 63.5% in T1 to 77.9% in T3; in G2, from 67.3% to 77.9%; in G3, from 71.2% to 75.4%. In G1, BCi increased from 110.4% to 140.0%; in G2, from 122.8% to 140.1%; in G3, from 133.3% to 139.2%. An average increase in 1.0% in CCi was associated with an average increase in functional capacity of 0.37 METS.<br><br>CONCLUSIONS: Chronotropic index significantly improves with CRP, irrespective of BB dose changes. CCi is more closely related with improvement in functional capacity than BCi. Improvement of Ci is an important predictor of functional capacity and prognosis in cardiovascular disease patients.},
}
@article {pmid34920443,
year = {2022},
author = {Mattioli, F and Porcaro, C and Baldassarre, G},
title = {A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface.},
journal = {Journal of neural engineering},
volume = {18},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac4430},
pmid = {34920443},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagination ; *Machine Learning ; *Movement ; *Neural Networks, Computer ; Sensorimotor Cortex/physiology ; },
abstract = {Objective.Brain-computer interface (BCI) aims to establish communication paths between the brain processes and external devices. Different methods have been used to extract human intentions from electroencephalography (EEG) recordings. Those based on motor imagery (MI) seem to have a great potential for future applications. These approaches rely on the extraction of EEG distinctive patterns during imagined movements. Techniques able to extract patterns from raw signals represent an important target for BCI as they do not need labor-intensive data pre-processing.Approach.We propose a new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a 'baseline' class) using a data augmentation algorithm and a limited number of EEG channels. In addition, we present a transfer learning method used to extract critical features from the EEG group dataset and then to customize the model to the single individual by training its late layers with only 12-min individual-related data.Main results.The model tested with the 'EEG Motor Movement/Imagery Dataset' outperforms the current state-of-the-art models by achieving a99.38%accuracy at the group level. In addition, the transfer learning approach we present achieves an average accuracy of99.46%.Significance.The proposed methods could foster the development of future BCI applications relying on few-channel portable recording devices and individual-based training.},
}
@article {pmid34916905,
year = {2021},
author = {Xu, B and Deng, L and Zhang, D and Xue, M and Li, H and Zeng, H and Song, A},
title = {Electroencephalogram Source Imaging and Brain Network Based Natural Grasps Decoding.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {797990},
pmid = {34916905},
issn = {1662-4548},
abstract = {Studying the decoding process of complex grasping movement is of great significance to the field of motor rehabilitation. This study aims to decode five natural reach-and-grasp types using sources of movement-related cortical potential (MRCP) and investigate their difference in cortical signal characteristics and network structures. Electroencephalogram signals were gathered from 40 channels of eight healthy subjects. In an audio cue-based experiment, subjects were instructed to keep no-movement condition or perform five natural reach-and-grasp movements: palmar, pinch, push, twist and plug. We projected MRCP into source space and used average source amplitudes in 24 regions of interest as classification features. Besides, functional connectivity was calculated using phase locking value. Six-class classification results showed that a similar grand average peak performance of 49.35% can be achieved using source features, with only two-thirds of the number of channel features. Besides, source imaging maps and brain networks presented different patterns between each condition. Grasping pattern analysis indicated that the modules in the execution stage focus more on internal communication than in the planning stage. The former stage was related to the parietal lobe, whereas the latter was associated with the frontal lobe. This study demonstrates the superiority and effectiveness of source imaging technology and reveals the spread mechanism and network structure of five natural reach-and-grasp movements. We believe that our work will contribute to the understanding of the generation mechanism of grasping movement and promote a natural and intuitive control of brain-computer interface.},
}
@article {pmid34916587,
year = {2021},
author = {Batzianoulis, I and Iwane, F and Wei, S and Correia, CGPR and Chavarriaga, R and Millán, JDR and Billard, A},
title = {Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials.},
journal = {Communications biology},
volume = {4},
number = {1},
pages = {1406},
pmid = {34916587},
issn = {2399-3642},
mesh = {Adult ; Humans ; *Learning ; Male ; *Reinforcement, Psychology ; Robotics/*methods ; },
abstract = {Robotic assistance via motorized robotic arm manipulators can be of valuable assistance to individuals with upper-limb motor disabilities. Brain-computer interfaces (BCI) offer an intuitive means to control such assistive robotic manipulators. However, BCI performance may vary due to the non-stationary nature of the electroencephalogram (EEG) signals. It, hence, cannot be used safely for controlling tasks where errors may be detrimental to the user. Avoiding obstacles is one such task. As there exist many techniques to avoid obstacles in robotics, we propose to give the control to the robot to avoid obstacles and to leave to the user the choice of the robot behavior to do so a matter of personal preference as some users may be more daring while others more careful. We enable the users to train the robot controller to adapt its way to approach obstacles relying on BCI that detects error-related potentials (ErrP), indicative of the user's error expectation of the robot's current strategy to meet their preferences. Gaussian process-based inverse reinforcement learning, in combination with the ErrP-BCI, infers the user's preference and updates the obstacle avoidance controller so as to generate personalized robot trajectories. We validate the approach in experiments with thirteen able-bodied subjects using a robotic arm that picks up, places and avoids real-life objects. Results show that the algorithm can learn user's preference and adapt the robot behavior rapidly using less than five demonstrations not necessarily optimal.},
}
@article {pmid34915580,
year = {2022},
author = {Itoh, TD and Ishihara, K and Morimoto, J},
title = {Implicit Contact Dynamics Modeling With Explicit Inertia Matrix Representation for Real-Time, Model-Based Control in Physical Environment.},
journal = {Neural computation},
volume = {34},
number = {2},
pages = {360-377},
doi = {10.1162/neco_a_01465},
pmid = {34915580},
issn = {1530-888X},
mesh = {Learning ; Motion ; Neural Networks, Computer ; *Robotics/methods ; },
abstract = {Model-based control has great potential for use in real robots due to its high sampling efficiency. Nevertheless, dealing with physical contacts and generating accurate motions are inevitable for practical robot control tasks, such as precise manipulation. For a real-time, model-based approach, the difficulty of contact-rich tasks that requires precise movement lies in the fact that a model needs to accurately predict forthcoming contact events within a limited length of time rather than detect them afterward with sensors. Therefore, in this study, we investigate whether and how neural network models can learn a task-related model useful enough for model-based control, that is, a model predicting future states, including contact events. To this end, we propose a structured neural network model predictive control (SNN-MPC) method, whose neural network architecture is designed with explicit inertia matrix representation. To train the proposed network, we develop a two-stage modeling procedure for contact-rich dynamics from a limited number of samples. As a contact-rich task, we take up a trackball manipulation task using a physical 3-DoF finger robot. The results showed that the SNN-MPC outperformed MPC with a conventional fully connected network model on the manipulation task.},
}
@article {pmid34915046,
year = {2022},
author = {Tan, Y and Lin, Y and Zang, B and Gao, X and Yong, Y and Yang, J and Li, S},
title = {An autonomous hybrid brain-computer interface system combined with eye-tracking in virtual environment.},
journal = {Journal of neuroscience methods},
volume = {368},
number = {},
pages = {109442},
doi = {10.1016/j.jneumeth.2021.109442},
pmid = {34915046},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Eye-Tracking Technology ; Fixation, Ocular ; Humans ; Photic Stimulation ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) has become an effective human-machine interactive way. However, the performance of the traditional BCI system needs to be further improved, such as flexibility, robustness, and accuracy. We aim to develop an autonomous hybrid BCI system combined with eye-tracking for the control tasks in the virtual environment.<br><br>NEW METHOD: This work developed an autonomous control strategy and proposed an effective fusion method for electroencephalogram (EEG) and eye tracking. For the autonomous control, the sliding window method was adopted to analyze the user's eye-gaze data. When the variance of eye-gaze data was less than the threshold, target recognition was triggered. EEG and eye-gaze data were synchronously collected and fused for classification. In addition, a fusion method based on particle swarm optimization (PSO) was proposed, which can find the best fusion weights to adapt to the differences of single modalities.<br><br>RESULTS: EEG data and eye-gaze data of 15 subjects in steady-state visual evoked potentials (SSVEP) tasks were collected to evaluate the effectiveness of the hybrid BCI system. The results showed that the PSO fusion method performed best in all fusion methods. And the proposed hybrid BCI system obtained higher accuracy and information transfer rate (ITR) than the single-modality.<br><br>The PSO fusion method was compared with average weighting fusion, prior weighting fusion, support vector machine, decision tree, random forest, and extreme random tree.<br><br>CONCLUSION: The proposed methods of autonomous control and dual-modal fusion can improve the flexibility, robustness and classification performance of the hybrid BCI system.},
}
@article {pmid34913508,
year = {2022},
author = {Fan, J and Shi, J and Zhang, Y and Liu, J and An, C and Zhu, H and Wu, P and Hu, W and Qin, R and Yao, D and Shou, X and Xu, Y and Tong, Z and Wen, X and Xu, J and Zhang, J and Fang, W and Lou, J and Yin, W and Chen, W},
title = {NKG2D discriminates diverse ligands through selectively mechano-regulated ligand conformational changes.},
journal = {The EMBO journal},
volume = {41},
number = {2},
pages = {e107739},
pmid = {34913508},
issn = {1460-2075},
mesh = {Binding Sites ; Cells, Cultured ; Histocompatibility Antigens Class I/chemistry/metabolism ; Humans ; K562 Cells ; Ligands ; Mechanical Phenomena ; Molecular Dynamics Simulation ; NK Cell Lectin-Like Receptor Subfamily K/*chemistry/metabolism ; Protein Binding ; Single Molecule Imaging ; },
abstract = {Stimulatory immune receptor NKG2D binds diverse ligands to elicit differential anti-tumor and anti-virus immune responses. Two conflicting degeneracy recognition models based on static crystal structures and in-solution binding affinities have been considered for almost two decades. Whether and how NKG2D recognizes and discriminates diverse ligands still remain unclear. Using live-cell-based single-molecule biomechanical assay, we characterized the in situ binding kinetics of NKG2D interacting with different ligands in the absence or presence of mechanical force. We found that mechanical force application selectively prolonged NKG2D interaction lifetimes with the ligands MICA and MICB, but not with ULBPs, and that force-strengthened binding is much more pronounced for MICA than for other ligands. We also integrated steered molecular dynamics simulations and mutagenesis to reveal force-induced rotational conformational changes of MICA, involving formation of additional hydrogen bonds on its binding interface with NKG2D, impeding MICA dissociation under force. We further provided a kinetic triggering model to reveal that force-dependent affinity determines NKG2D ligand discrimination and its downstream NK cell activation. Together, our results demonstrate that NKG2D has a discrimination power to recognize different ligands, which depends on selective mechanical force-induced ligand conformational changes.},
}
@article {pmid34912883,
year = {2021},
author = {Simões, FB and Kmit, A and Amaral, MD},
title = {Cross-talk of inflammatory mediators and airway epithelium reveals the cystic fibrosis transmembrane conductance regulator as a major target.},
journal = {ERJ open research},
volume = {7},
number = {4},
pages = {},
pmid = {34912883},
issn = {2312-0541},
abstract = {Airway inflammation, mucus hyperproduction and epithelial remodelling are hallmarks of many chronic airway diseases, including asthma, COPD and cystic fibrosis. While several cytokines are dysregulated in these diseases, most studies focus on the response of airways to interleukin (IL)-4 and IL-13, which have been shown to induce mucus hyperproduction and shift the airway epithelium towards a hypersecretory phenotype. We hypothesised that other cytokines might induce the expression of chloride (Cl-) channels/transporters, and regulate epithelial differentiation and mucus production. To this end, fully differentiated human airway basal cells (BCi-NS1.1) were treated with cytokines identified as dysregulated in those diseases, namely IL-8, IL-1β, IL-4, IL-17A, IL-10 and IL-22, and tumour necrosis factor-α. Our results show that the cystic fibrosis transmembrane conductance regulator (CFTR) is the main Cl- channel modulated by inflammation, in contrast to transmembrane protein 16A (TMEM16A), whose levels only changed with IL-4. Furthermore, we identified novel roles for IL-10 and IL-22 by influencing epithelial differentiation towards ciliated cells and away from pulmonary ionocytes. In contrast, IL-1β and IL-4 reduced the number of ciliated cells while increasing club cells. Interestingly, while IL-1β, IL-4 and IL-10 upregulated CFTR expression, IL-4 was the only cytokine that increased both its function and the number of CFTR-expressing club cells, suggesting that this cell type may be the main contributor for CFTR function. Additionally, all cytokines assessed increased mucus production through a differential upregulation of MUC5AC and MUC5B transcript levels. This study reveals a novel insight into differentiation resulting from the cross-talk of inflammatory mediators and airway epithelial cells, which is particularly relevant for chronic airway diseases.},
}
@article {pmid34912203,
year = {2021},
author = {Wei, CS and Keller, CJ and Li, J and Lin, YP and Nakanishi, M and Wagner, J and Wu, W and Zhang, Y and Jung, TP},
title = {Editorial: Inter- and Intra-subject Variability in Brain Imaging and Decoding.},
journal = {Frontiers in computational neuroscience},
volume = {15},
number = {},
pages = {791129},
doi = {10.3389/fncom.2021.791129},
pmid = {34912203},
issn = {1662-5188},
}
@article {pmid34910975,
year = {2022},
author = {López-García, D and Peñalver, JMG and Górriz, JM and Ruz, M},
title = {MVPAlab: A machine learning decoding toolbox for multidimensional electroencephalography data.},
journal = {Computer methods and programs in biomedicine},
volume = {214},
number = {},
pages = {106549},
doi = {10.1016/j.cmpb.2021.106549},
pmid = {34910975},
issn = {1872-7565},
mesh = {Algorithms ; Brain ; *Electroencephalography ; Machine Learning ; *Magnetoencephalography ; Software ; },
abstract = {BACKGROUND AND OBJECTIVE: The study of brain function has recently expanded from classical univariate to multivariate analyses. These multivariate, machine learning-based algorithms afford neuroscientists extracting more detailed and richer information from the data. However, the implementation of these procedures is usually challenging, especially for researchers with no coding experience. To address this problem, we have developed MVPAlab, a MATLAB-based, flexible decoding toolbox for multidimensional electroencephalography and magnetoencephalography data.<br><br>METHODS: The MVPAlab Toolbox implements several machine learning algorithms to compute multivariate pattern analyses, cross-classification, temporal generalization matrices and feature and frequency contribution analyses. It also provides access to an extensive set of preprocessing routines for, among others, data normalization, data smoothing, dimensionality reduction and supertrial generation. To draw statistical inferences at the group level, MVPAlab includes a non-parametric cluster-based permutation approach.<br><br>RESULTS: A sample electroencephalography dataset was compiled to test all the MVPAlab main functionalities. Significant clusters (p<0.01) were found for the proposed decoding analyses and different configurations, proving the software capability for discriminating between different experimental conditions.<br><br>CONCLUSIONS: This toolbox has been designed to include an easy-to-use and intuitive graphic user interface and data representation software, which makes MVPAlab a very convenient tool for users with few or no previous coding experience. In addition, MVPAlab is not for beginners only, as it implements several high and low-level routines allowing more experienced users to design their own projects in a highly flexible manner.},
}
@article {pmid34906019,
year = {2022},
author = {Cuomo, G and Maglianella, V and Ghanbari Ghooshchy, S and Zoccolotti, P and Martelli, M and Paolucci, S and Morone, G and Iosa, M},
title = {Motor imagery and gait control in Parkinson's disease: techniques and new perspectives in neurorehabilitation.},
journal = {Expert review of neurotherapeutics},
volume = {22},
number = {1},
pages = {43-51},
doi = {10.1080/14737175.2022.2018301},
pmid = {34906019},
issn = {1744-8360},
mesh = {Gait ; Humans ; Imagery, Psychotherapy/methods ; *Neurological Rehabilitation ; *Parkinson Disease ; *Virtual Reality ; },
abstract = {INTRODUCTION: Motor imagery (MI), defined as the ability to mentally represent an action without actual movement, has been used to improve motor function in athletes and, more recently, in neurological disorders such as Parkinson's disease (PD). Several studies have investigated the neural correlates of motor imagery, which change also depending on the action imagined.<br><br>AREAS COVERED: This review focuses on locomotion, which is a crucial activity in everyday life and is often impaired by neurological conditions. After a general discussion on the neural correlates of motor imagery and locomotion, we review the evidence highlighting the abnormalities in gait control and gait imagery in PD patients. Next, new perspectives and techniques for PD patients' rehabilitation are discussed, namely Brain Computer Interfaces (BCIs), neurofeedback, and virtual reality (VR).<br><br>EXPERT OPINION: Despite the few studies, the literature review supports the potential beneficial effects of motor imagery interventions in PD focused on locomotion. The development of new technologies could empower the administration of training based on motor imagery locomotor tasks, and their application could lead to new rehabilitation protocols aimed at improving walking ability in patients with PD.},
}
@article {pmid34905967,
year = {2021},
author = {Mousavi J S, SM and Faghihi, D and Sommer, K and Bhurwani, MMS and Patel, TR and Santo, B and Waqas, M and Ionita, C and Levy, EI and Siddiqui, AH and Tutino, VM},
title = {Realistic computer modelling of stent retriever thrombectomy: a hybrid finite-element analysis-smoothed particle hydrodynamics model.},
journal = {Journal of the Royal Society, Interface},
volume = {18},
number = {185},
pages = {20210583},
pmid = {34905967},
issn = {1742-5662},
support = {UL1 TR001412/TR/NCATS NIH HHS/United States ; },
mesh = {*Brain Ischemia ; Computers ; Humans ; Hydrodynamics ; Stents ; *Stroke ; Thrombectomy ; Treatment Outcome ; },
abstract = {Stent retriever thrombectomy is a pre-eminent treatment modality for large vessel ischaemic stroke. Simulation of thrombectomy could help understand stent and clot mechanics in failed cases and provide a digital testbed for the development of new, safer devices. Here, we present a novel, in silico thrombectomy method using a hybrid finite-element analysis (FEA) and smoothed particle hydrodynamics (SPH). Inspired by its biological structure and components, the blood clot was modelled with the hybrid FEA-SPH method. The Solitaire self-expanding stent was parametrically reconstructed from micro-CT imaging and was modelled as three-dimensional finite beam elements. Our simulation encompassed all steps of mechanical thrombectomy, including stent packaging, delivery and self-expansion into the clot, and clot extraction. To test the feasibility of our method, we simulated clot extraction in simple straight vessels. This was compared against in vitro thrombectomies using the same stent, vessel geometry, and clot size and composition. Comparisons with benchtop tests indicated that our model was able to accurately simulate clot deflection and penetration of stent wires into the clot, the relative movement of the clot and stent during extraction, and clot fragmentation/embolus formation. In this study, we demonstrated that coupling FEA and SPH techniques could realistically model stent retriever thrombectomy.},
}
@article {pmid34902850,
year = {2021},
author = {Li, S and Jin, J and Daly, I and Liu, C and Cichocki, A},
title = {Feature selection method based on Menger curvature and LDA theory for a P300 brain-computer interface.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ac42b4},
pmid = {34902850},
issn = {1741-2552},
abstract = {Brain-computer interface (BCI) systems decode electroencephalogram signals to establish a channel for direct interaction between the human brain and the external world without the need for muscle or nerve control. The P300 speller, one of the most widely used BCI applications, presents a selection of characters to the user and performs character recognition by identifying P300 event-related potentials from the EEG. Such P300-based BCI systems can reach good levels of accuracy but are difficult to use in day-to-day life due to redundancy and noisy signal. A room for improvement should be considered. We propose a novel hybrid feature selection method for the P300-based BCI system to address the problem of feature redundancy, which combines the Menger curvature and linear discriminant analysis. First, selected strategies are applied separately to a given dataset to estimate the gain for application to each feature. Then, each generated value set is ranked in descending order and judged by a predefined criterion to be suitable in classification models. The intersection of the two approaches is then evaluated to identify an optimal feature subset. The proposed method is evaluated using three public datasets, i.e., BCI Competition III dataset II, BNCI Horizon dataset, and EPFL dataset. Experimental results indicate that compared with other typical feature selection and classification methods, our proposed method has better or comparable performance. Additionally, our proposed method can achieve the best classification accuracy after all epochs in three datasets. In summary, our proposed method provides a new way to enhance the performance of the P300-based BCI speller.},
}
@article {pmid34902609,
year = {2021},
author = {Li, R and Ren, C and Zhang, X and Hu, B},
title = {A novel ensemble learning method using multiple objective particle swarm optimization for subject-independent EEG-based emotion recognition.},
journal = {Computers in biology and medicine},
volume = {140},
number = {},
pages = {105080},
doi = {10.1016/j.compbiomed.2021.105080},
pmid = {34902609},
issn = {1879-0534},
abstract = {Emotion recognition is a vital but challenging step in creating passive brain-computer interface applications. In recent years, many studies on electroencephalogram (EEG)-based emotion recognition have been conducted. Ensemble learning has been widely used in emotion recognition because of its superior accuracy and generalization. In this study, we proposed a novel ensemble learning method based on multiple objective particle swarm optimization for subject-independent EEG-based emotion recognition. First, we used a 4 s sliding time window with a 2 s overlap to extract 13 different features from EEG signals and construct a feature vector. Then, we employed L1 regularization to select effective features. Second, a model selection method was applied to choose the optimal basic analysis submodels. Afterward, we proposed an ensemble operator that converts the classification results of a single model from discrete values to continuous values to better characterize the classification results. Subsequently, multiple objective particle swarm optimization was adopted to confirm the optimal parameters of the ensemble learning model. Finally, we conducted extensive experiments on two public datasets: DEAP and SEED. Considering the generalization of the model, we applied leave-one-subject-out cross-validation to evaluate the performance of the model. The experimental results demonstrate that the proposed method achieves a better recognition performance than single methods, commonly used ensemble learning methods, and state-of-the-art methods. The average accuracies for arousal and valence are 65.70% and 64.22%, respectively, on the DEAP database, and the average accuracy on the SEED database is 84.44%.},
}
@article {pmid34902364,
year = {2022},
author = {Shen, L and Xia, Y and Li, Y and Sun, M},
title = {A multiscale siamese convolutional neural network with cross-channel fusion for motor imagery decoding.},
journal = {Journal of neuroscience methods},
volume = {367},
number = {},
pages = {109426},
doi = {10.1016/j.jneumeth.2021.109426},
pmid = {34902364},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination ; Neural Networks, Computer ; },
abstract = {BACKGROUND: Recently, convolutional neural networks (CNN) are widely applied in motor imagery electroencephalography (MI-EEG) signal classification tasks. However, a simple CNN framework is challenging to satisfy the complex MI-EEG signal decoding.<br><br>NEW METHOD: In this study, we propose a multiscale Siamese convolutional neural network with cross-channel fusion (MSCCF-Net) for MI-EEG classification tasks. The proposed network consists of three parts: Siamese cross-channel fusion streams, similarity module and classification module. Each Siamese cross-channel fusion stream contains multiple branches, and each branch is supplemented by cross-channel fusion modules to improve multiscale temporal feature representation capability. The similarity module is adopted to measure the feature similarity between multiple branches. At the same time, the classification module provides a strong constraint to classify the features from all Siamese cross-channel fusion streams. The combination of the similarity module and classification module constitutes a new joint training strategy to further optimize the network performance.<br><br>RESULTS: The experiment is conducted on the public BCI Competition IV 2a and 2b datasets, and the results show that the proposed network achieves an average accuracy of 87.36% and 87.33%, respectively.<br><br>The proposed network adopts cross-channel fusion to learn multiscale temporal characteristics and joint training strategy to optimize the training process. Therefore, the performance outperforms other state-of-the-art MI-EEG signal classification methods.},
}
@article {pmid34901837,
year = {2021},
author = {Yang, J and Wang, YK and Yao, X and Lin, CT},
title = {Adaptive Initialization Method for K-Means Algorithm.},
journal = {Frontiers in artificial intelligence},
volume = {4},
number = {},
pages = {740817},
pmid = {34901837},
issn = {2624-8212},
abstract = {The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses a random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. In this research, we propose an adaptive initialization method for the K-means algorithm (AIMK) which can adapt to the various characteristics in different datasets and obtain better clustering performance with stable results. For larger or higher-dimensional datasets, we even leverage random sampling in AIMK (name as AIMK-RS) to reduce the time complexity. 22 real-world datasets were applied for performance comparisons. The experimental results show AIMK and AIMK-RS outperform the current initialization methods and several well-known clustering algorithms. Specifically, AIMK-RS can significantly reduce the time complexity to O (n). Moreover, we exploit AIMK to initialize K-medoids and spectral clustering, and better performance is also explored. The above results demonstrate superior performance and good scalability by AIMK or AIMK-RS. In the future, we would like to apply AIMK to more partition-based clustering algorithms to solve real-life practical problems.},
}
@article {pmid34900346,
year = {2021},
author = {Zhou, Z and Gong, A and Qian, Q and Su, L and Zhao, L and Fu, Y},
title = {A novel strategy for driving car brain-computer interfaces: Discrimination of EEG-based visual-motor imagery.},
journal = {Translational neuroscience},
volume = {12},
number = {1},
pages = {482-493},
pmid = {34900346},
issn = {2081-3856},
abstract = {A brain-computer interface (BCI) based on kinesthetic motor imagery has a potential of becoming a groundbreaking technology in a clinical setting. However, few studies focus on a visual-motor imagery (VMI) paradigm driving BCI. The VMI-BCI feature extraction methods are yet to be explored in depth. In this study, a novel VMI-BCI paradigm is proposed to execute four VMI tasks: imagining a car moving forward, reversing, turning left, and turning right. These mental strategies can naturally control a car or robot to move forward, backward, left, and right. Electroencephalogram (EEG) data from 25 subjects were collected. After the raw EEG signal baseline was corrected, the alpha band was extracted using bandpass filtering. The artifacts were removed by independent component analysis. Then, the EEG average instantaneous energy induced by VMI (VMI-EEG) was calculated using the Hilbert-Huang transform (HHT). The autoregressive model was extracted to construct a 12-dimensional feature vector to a support vector machine suitable for small sample classification. This was classified into two-class tasks: visual imagination of driving the car forward versus reversing, driving forward versus turning left, driving forward versus turning right, reversing versus turning left, reversing versus turning right, and turning left versus turning right. The results showed that the average classification accuracy of these two-class tasks was 62.68 ± 5.08%, and the highest classification accuracy was 73.66 ± 6.80%. The study showed that EEG features of O1 and O2 electrodes in the occipital region extracted by HHT were separable for these VMI tasks.},
}
@article {pmid34899223,
year = {2021},
author = {Nagels-Coune, L and Riecke, L and Benitez-Andonegui, A and Klinkhammer, S and Goebel, R and De Weerd, P and Lührs, M and Sorger, B},
title = {See, Hear, or Feel - to Speak: A Versatile Multiple-Choice Functional Near-Infrared Spectroscopy-Brain-Computer Interface Feasible With Visual, Auditory, or Tactile Instructions.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {784522},
pmid = {34899223},
issn = {1662-5161},
abstract = {Severely motor-disabled patients, such as those suffering from the so-called "locked-in" syndrome, cannot communicate naturally. They may benefit from brain-computer interfaces (BCIs) exploiting brain signals for communication and therewith circumventing the muscular system. One BCI technique that has gained attention recently is functional near-infrared spectroscopy (fNIRS). Typically, fNIRS-based BCIs allow for brain-based communication via voluntarily modulation of brain activity through mental task performance guided by visual or auditory instructions. While the development of fNIRS-BCIs has made great progress, the reliability of fNIRS-BCIs across time and environments has rarely been assessed. In the present fNIRS-BCI study, we tested six healthy participants across three consecutive days using a straightforward four-choice fNIRS-BCI communication paradigm that allows answer encoding based on instructions using various sensory modalities. To encode an answer, participants performed a motor imagery task (mental drawing) in one out of four time periods. Answer encoding was guided by either the visual, auditory, or tactile sensory modality. Two participants were tested outside the laboratory in a cafeteria. Answers were decoded from the time course of the most-informative fNIRS channel-by-chromophore combination. Across the three testing days, we obtained mean single- and multi-trial (joint analysis of four consecutive trials) accuracies of 62.5 and 85.19%, respectively. Obtained multi-trial accuracies were 86.11% for visual, 80.56% for auditory, and 88.89% for tactile sensory encoding. The two participants that used the fNIRS-BCI in a cafeteria obtained the best single- (72.22 and 77.78%) and multi-trial accuracies (100 and 94.44%). Communication was reliable over the three recording sessions with multi-trial accuracies of 86.11% on day 1, 86.11% on day 2, and 83.33% on day 3. To gauge the trade-off between number of optodes and decoding accuracy, averaging across two and three promising fNIRS channels was compared to the one-channel approach. Multi-trial accuracy increased from 85.19% (one-channel approach) to 91.67% (two-/three-channel approach). In sum, the presented fNIRS-BCI yielded robust decoding results using three alternative sensory encoding modalities. Further, fNIRS-BCI communication was stable over the course of three consecutive days, even in a natural (social) environment. Therewith, the developed fNIRS-BCI demonstrated high flexibility, reliability and robustness, crucial requirements for future clinical applicability.},
}
@article {pmid34899220,
year = {2021},
author = {Gutierrez-Martinez, J and Mercado-Gutierrez, JA and Carvajal-Gámez, BE and Rosas-Trigueros, JL and Contreras-Martinez, AE},
title = {Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {772837},
pmid = {34899220},
issn = {1662-5161},
abstract = {Brain-Computer Interface (BCI) is a technology that uses electroencephalographic (EEG) signals to control external devices, such as Functional Electrical Stimulation (FES). Visual BCI paradigms based on P300 and Steady State Visually Evoked potentials (SSVEP) have shown high potential for clinical purposes. Numerous studies have been published on P300- and SSVEP-based non-invasive BCIs, but many of them present two shortcomings: (1) they are not aimed for motor rehabilitation applications, and (2) they do not report in detail the artificial intelligence (AI) methods used for classification, or their performance metrics. To address this gap, in this paper the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was applied to prepare a systematic literature review (SLR). Papers older than 10 years, repeated or not related to a motor rehabilitation application, were excluded. Of all the studies, 51.02% referred to theoretical analysis of classification algorithms. Of the remaining, 28.48% were for spelling, 12.73% for diverse applications (control of wheelchair or home appliances), and only 7.77% were focused on motor rehabilitation. After the inclusion and exclusion criteria were applied and quality screening was performed, 34 articles were selected. Of them, 26.47% used the P300 and 55.8% the SSVEP signal. Five applications categories were established: Rehabilitation Systems (17.64%), Virtual Reality environments (23.52%), FES (17.64%), Orthosis (29.41%), and Prosthesis (11.76%). Of all the works, only four performed tests with patients. The most reported machine learning (ML) algorithms used for classification were linear discriminant analysis (LDA) (48.64%) and support vector machine (16.21%), while only one study used a deep learning algorithm: a Convolutional Neural Network (CNN). The reported accuracy ranged from 38.02 to 100%, and the Information Transfer Rate from 1.55 to 49.25 bits per minute. While LDA is still the most used AI algorithm, CNN has shown promising results, but due to their high technical implementation requirements, many researchers do not justify its implementation as worthwile. To achieve quick and accurate online BCIs for motor rehabilitation applications, future works on SSVEP-, P300-based and hybrid BCIs should focus on optimizing the visual stimulation module and the training stage of ML and DL algorithms.},
}
@article {pmid34898449,
year = {2021},
author = {Chen, Y and Ji, M and Wu, Y and Wang, Q and Deng, Y and Liu, Y and Wu, F and Liu, M and Guo, Y and Fu, Z and Zheng, X},
title = {An Intelligent Individualized Cardiovascular App for Risk Elimination (iCARE) for Individuals With Coronary Heart Disease: Development and Usability Testing Analysis.},
journal = {JMIR mHealth and uHealth},
volume = {9},
number = {12},
pages = {e26439},
pmid = {34898449},
issn = {2291-5222},
mesh = {*Coronary Disease/prevention &amp; control ; Humans ; Male ; Middle Aged ; *Mobile Applications ; User-Centered Design ; User-Computer Interface ; *Wearable Electronic Devices ; },
abstract = {BACKGROUND: Death and disability from coronary heart disease (CHD) can be largely reduced by improving risk factor management. However, adhering to evidence-based recommendations is challenging and requires interventions at the level of the patient, provider, and health system.<br><br>OBJECTIVE: The aim of this study was to develop an Intelligent Individualized Cardiovascular App for Risk Elimination (iCARE) to facilitate adherence to health behaviors and preventive medications, and to test the usability of iCARE.<br><br>METHODS: We developed iCARE based on a user-centered design approach, which included 4 phases: (1) function design, (2) iterative design, (3) expert inspections and walkthroughs of the prototypes, and (4) usability testing with end users. The usability testing of iCARE included 2 stages: stage I, which included a task analysis and a usability evaluation (January to March 2019) of the iCARE patient app using the modified Health Information Technology Usability Survey (Health-ITUES); and stage II (June 2020), which used the Health-ITUES among end users who used the app for 6 months. The end users were individuals with a confirmed diagnosis of CHD from 2 university-affiliated hospitals in Beijing, China.<br><br>RESULTS: iCARE consists of a patient app, a care provider app, and a cloud platform. It has a set of algorithms that trigger tailored feedback and can send individualized interventions based on data from initial assessment and health monitoring via manual entry or wearable devices. For stage I usability testing, 88 hospitalized patients (72% [63/88] male; mean age 60 [SD 9.9] years) with CHD were included in the study. The mean score of the usability testing was 90.1 (interquartile range 83.3-99.0). Among enrolled participants, 90% (79/88) were satisfied with iCARE; 94% (83/88) and 82% (72/88) reported that iCARE was useful and easy to use, respectively. For stage II usability testing, 61 individuals with CHD (85% [52/61] male; mean age 53 [SD 8.2] years) who were from an intervention arm and used iCARE for at least six months were included. The mean total score on usability testing based on the questionnaire was 89.0 (interquartile distance: 77.0-99.5). Among enrolled participants, 89% (54/61) were satisfied with the use of iCARE, 93% (57/61) perceived it as useful, and 70% (43/61) as easy to use.<br><br>CONCLUSIONS: This study developed an intelligent, individualized, evidence-based, and theory-driven app (iCARE) to improve patients' adherence to health behaviors and medication management. iCARE was identified to be highly acceptable, useful, and easy to use among individuals with a diagnosis of CHD.<br><br>TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR-INR-16010242; https://tinyurl.com/2p8bkrew.},
}
@article {pmid34895161,
year = {2021},
author = {Argante, L and Abbing-Karahagopian, V and Vadivelu, K and Rappuoli, R and Medini, D},
title = {A re-assessment of 4CMenB vaccine effectiveness against serogroup B invasive meningococcal disease in England based on an incidence model.},
journal = {BMC infectious diseases},
volume = {21},
number = {1},
pages = {1244},
pmid = {34895161},
issn = {1471-2334},
mesh = {Bayes Theorem ; England/epidemiology ; Humans ; Incidence ; Infant ; *Meningococcal Infections/epidemiology/prevention &amp; control ; *Meningococcal Vaccines ; *Neisseria meningitidis, Serogroup B ; Serogroup ; Vaccine Efficacy ; },
abstract = {BACKGROUND: The four-component serogroup B meningococcal 4CMenB vaccine (Bexsero, GSK) has been routinely given to all infants in the United Kingdom at 2, 4 and 12 months of age since September 2015. After 3 years, Public Health England (PHE) reported a 75% [95% confidence interval 64%; 81%] reduction in the incidence of serogroup B invasive meningococcal disease (IMD) in age groups eligible to be fully vaccinated. In contrast, vaccine effectiveness (VE) evaluated in the same immunization program applying the screening method was not statistically significant. We re-analyzed the data using an incidence model.<br><br>METHODS: Aggregate data-stratified by age, year and doses received-were provided by PHE: serogroup B IMD case counts for the entire population of England (years 2011-2018) and 4CMenB vaccine uptake in infants. We combined uptake with national population estimates to obtain counts of vaccinated and unvaccinated person-time by age and time. We re-estimated VE comparing incidence rates in vaccinated and non-vaccinated subjects using a Bayesian Poisson model for case counts with person-time data as an offset. The model was adjusted for age, time and number of doses received.<br><br>RESULTS: The incidence model showed that cases decreased until 2013-2014, followed by an increasing trend that continued in the non-vaccinated population during the immunization program. VE in fully vaccinated subjects (three doses) was 80.1% [95% Bayesian credible interval (BCI): 70.3%; 86.7%]. After a single dose, VE was 33.5% [12.4%; 49.7%]95%BCI and after two doses, 78.7% [71.5%; 84.5%]95%BCI. We estimated that vaccination averted 312 cases [252; 368]95%BCI between 2015 and 2018. VE was in line with the previously reported incidence reduction.<br><br>CONCLUSIONS: Our estimates of VE had higher precision than previous estimates based on the screening method, which were statistically not significant, and in line with the 75% incidence reduction previously reported by PHE. When disease incidence is low and vaccine uptake is high, the screening method applied to cases exclusively from the population eligible for vaccination may not be precise enough and may produce misleading point-estimates. Precise and accurate VE estimates are fundamental to inform public health decision making. VE assessment can be enhanced using models that leverage data on subjects not eligible for vaccination.},
}
@article {pmid34892854,
year = {2021},
author = {Xu, T and Wang, X and Wang, J and Zhou, Y},
title = {From Textbook to Teacher: an Adaptive Intelligent Tutoring System Based on BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {7621-7624},
doi = {10.1109/EMBC46164.2021.9629483},
pmid = {34892854},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; Learning ; Students ; },
abstract = {In this work, we propose FT3, an adaptive intelligent tutoring system based on Brain Computer Interface(BCI). It can automatically generate different difficulty levels of lecturing video with teachers from textbook adapting to student engagement measured by BCI. Most current studies employ animated images to create pedagogical agents in such adaptive learning environments. However, evidence suggests that human teacher video brings a better learning experience than animated images. We design a virtual teacher generation engine consisting of text-to-speech (TTS) and lip synthesis method, being able to generate high-quality adaptive lecturing clips of talking teachers with accurate lip sync merely based on a textbook and teacher's photo. We propose a BCI to measure engagement, serving as an indicator for adaptively generating appropriate lecturing videos. We conduct a preliminary study to build and evaluate FT3. Results verify that FT3 can generate synced lecturing videos, and provide proper levels of learning content with an accuracy of 73.33%.},
}
@article {pmid34892761,
year = {2021},
author = {Chuang, J},
title = {Neural Dynamics of a Single Human with Long-Term, High Temporal Density Electroencephalography.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {7199-7205},
doi = {10.1109/EMBC46164.2021.9630280},
pmid = {34892761},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Cluster Analysis ; *Electroencephalography ; Humans ; Longitudinal Studies ; Neurophysiology ; },
abstract = {We undertake a longitudinal study with high temporal recording density, capturing daily electroencephalograms (EEG) of an individual in an in-situ setting for 370 consecutive days. Resting-state EEG retains a high level of stability over the course of the year, and inter-session variability remains unchanged, whether the sessions are one day, one week, or one month apart. On the other hand, EEG for certain cognitive tasks experience a steady decline in similarity over the same time period. Clustering analysis reveals that days with low similarity scores should not be considered as outliers, but instead are part of a cluster of days with a consistent alternate spectral signature. This has methodological and design implications for the selection of baseline references or templates in fields ranging from neurophysiology to brain-computer interfaces (BCI) and neurobiometrics.},
}
@article {pmid34892650,
year = {2021},
author = {Shen, X and Zhang, X and Wang, Y},
title = {Kernel Temporal Difference based Reinforcement Learning for Brain Machine Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6721-6724},
doi = {10.1109/EMBC46164.2021.9631086},
pmid = {34892650},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Humans ; Movement ; Reinforcement, Psychology ; Reward ; },
abstract = {Brain-machine interfaces (BMIs) enable people with disabilities to control external devices with their motor intentions through a decoder. Compared with supervised learning, reinforcement learning (RL) is more promising for the disabled because it can assist them to learn without actual limb movement. Current RL decoders deal with tasks with immediate reward delivery. But for tasks where the reward is only given by the end of the trial, existing RL methods may take a long time to train and are prone to becoming trapped in the local minima. In this paper, we propose to embed temporal difference method (TD) into Quantized Attention-Gated Kernel Reinforcement Learning (QAGKRL) to solve this temporal credit assignment problem. This algorithm utilizes a kernel network to ensure the global linear structure and adopts a softmax policy to efficiently explore the state-action mapping through TD error. We simulate a center-out task where the agent needs several steps to first reach a periphery target and then return to the center to get the external reward. Our proposed algorithm is tested on simulated data and compared with two state-of-the-art models. We find that introducing the TD method to QAGKRL achieves a prediction accuracy of 96.2% ± 0.77% (mean ± std), which is significantly better the other two methods.Clinical Relevance-This paper proposes a novel kernel temporal difference RL method for the multi-step task with delayed reward delivery, which potentially enables BMI online continuous decoding.},
}
@article {pmid34892645,
year = {2021},
author = {Tan, J and Shen, X and Zhang, X and Wang, Y},
title = {Multivariate Encoding Analysis of Medial Prefrontal Cortex Cortical Activity during Task Learning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6699-6702},
doi = {10.1109/EMBC46164.2021.9630322},
pmid = {34892645},
issn = {2694-0604},
mesh = {Animals ; *Brain-Computer Interfaces ; Learning ; *Neurons ; Prefrontal Cortex ; Rats ; Reward ; },
abstract = {Studies have shown that medial prefrontal cortex (mPFC) is responsible for outcome evaluation. Some recent studies also suggest that mPFC may play an important role in goal planning and action execution when performing a task. If the information encoded in mPFC can be accurately extracted and identified, it can improve the design of brain-machine interfaces by better reconstructing subjects' motion intention guided by reward information. In this paper, we investigate whether mPFC neural signals simultaneously encode information of goal planning, action execution and outcome evaluation. Linear-nonlinear-Poisson (LNP) model is applied for encoding analysis on mPFC neural spike data when a rat is learning a two-lever-press discrimination task. We use the L2-norm of tuning parameter in LNP model to indicate the importance of the encoded information and compare the spike train prediction performance of LNP model using all information, the most significant information and reward information only. The preliminary results indicate that mPFC activity can encode simultaneously the information of goal planning, action execution and outcome evaluation and that all the relevant information could be reconstructed from mPFC spike trains on a single trial basis.},
}
@article {pmid34892638,
year = {2021},
author = {Meng, J and Liu, J and Wang, H and Xu, M and Ming, D},
title = {Prediction Deviants with Varying Degrees Induce Separable Error-related EEG Features.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6671-6674},
doi = {10.1109/EMBC46164.2021.9630218},
pmid = {34892638},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Humans ; },
abstract = {Error-related potential (ErrP) usually emerges in the brain when human perceives errors and is believed to be a promising signal for optimizing brain-computer interface (BCI) system. However, most of the ErrP studies only focus on how to distinguish the correct and wrong conditions, which is not enough for the BCI application in real scenarios. Therefore, it is necessary to study the ErrPs induced by the prediction deviants with varying degrees, concurrently test the separability of such EEG features. To this end, electroencephalogram (EEG) data of twelve healthy subjects were recorded when they participated in a direction prediction experiment. There are three prediction -deviant conditions in it, i.e., correct prediction, 90°deviant, 180° deviant. Event-related potential and inter-trial coherence were analyzed. Consequently, the error-related negativity (ERN) and N450 component in FCZ were significantly modulated by the degrees of prediction deviants, especially in the low-frequency band (<13Hz). Moreover, single-trial classification was adopted to test the separability of these features; the averaged accuracies between any two conditions were 87.75%, 85.25%, 64.79%. This study demonstrates the prediction deviants with varying degrees can induce separable ErrP features, which provide a deeper understanding of the ErrP signatures for developing BCIs.},
}
@article {pmid34892635,
year = {2021},
author = {Montag, M and Paschall, C and Ojemann, J and Rao, R and Herron, J},
title = {A Platform for Virtual Reality Task Design with Intracranial Electrodes.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6659-6662},
doi = {10.1109/EMBC46164.2021.9630231},
pmid = {34892635},
issn = {2694-0604},
mesh = {Electrodes ; Electroencephalography ; Humans ; Movement ; Software ; *Virtual Reality ; },
abstract = {Research with human intracranial electrodes has traditionally been constrained by the limitations of the inpatient clinical setting. Immersive virtual reality (VR), however, can transcend setting and enable novel task design with precise control over visual and auditory stimuli. This control over visual and auditory feedback makes VR an exciting platform for new in-patient, human electrocorticography (ECOG) and stereo-electroencephalography (sEEG) research. The integration of intracranial electrode recording and stimulation with VR task dynamics required foundational systems engineering. In this work, we present a custom API that bridges Unity, the leading VR game development engine, and Synapse, the proprietary software of the Tucker Davis Technologies (TDT) neural recording and stimulation platform. To demonstrate the functionality and efficiency of our API, we developed a closed-loop brain-computer interface (BCI) task in which filtered neural signals controlled the movement of a virtual object and virtual object dynamics triggered neural stimulation. This closed-loop VR-BCI task confirmed the utility, safety, and efficacy of our API and its readiness for human task deployment.},
}
@article {pmid34892627,
year = {2021},
author = {Moslehi, AH and Davies, TC},
title = {EEG Electrode Selection for a Two-Class Motor Imagery Task in a BCI Using fNIRS Prior Data.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6627-6630},
doi = {10.1109/EMBC46164.2021.9630786},
pmid = {34892627},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Spectroscopy, Near-Infrared ; },
abstract = {This study investigated the possibility of using functional near infrared spectroscopy (fNIRS) during right- and left-hand motor imagery tasks to select an optimum set of electroencephalography (EEG) electrodes for a brain computer interface. fNIRS has better spatial resolution allowing areas of brain activity to more readily be identified. The ReliefF algorithm was used to identify the most reliable fNIRS channels. Then, EEG electrodes adjacent to those channels were selected for classification. This study used three different classifiers of linear and quadratic discriminant analyses, and support vector machine to examine the proposed method.Clinical Relevance- Reducing the number of sensors in a BCI makes the system more usable for patients with severe disabilities.},
}
@article {pmid34892625,
year = {2021},
author = {Zhang, X and Song, Z and Wang, Y},
title = {Reinforcement Learning-based Kalman Filter for Adaptive Brain Control in Brain-Machine Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6619-6622},
doi = {10.1109/EMBC46164.2021.9629511},
pmid = {34892625},
issn = {2694-0604},
mesh = {Animals ; Brain ; *Brain-Computer Interfaces ; Learning ; Rats ; Reinforcement, Psychology ; Reward ; },
abstract = {Brain-Machine Interfaces (BMIs) convert paralyzed people's neural signals into the command of the neuro-prosthesis. During the subject's brain control (BC) process, the neural patterns might change across time, making it crucial and challenging for the decoder to co-adapt with the dynamic neural patterns. Kalman Filter (KF) is commonly used for continuous control in BC. However, if the neural patterns become quite different compared with the training data, KF needs a re-calibration session to maintain its performance. On the other hand, Reinforcement Learning (RL) has the advantage of adaptive updating by the reward signal. But it is not very suitable for generating continuous motor states in BC due to the discrete action selection. In this paper, we propose a reinforcement learning-based Kalman filter. We maintain the state transition model of KF for a continuous motor state prediction. At the same time, we use RL to generate the action from the corresponding neural pattern, which is then used as a correction for the state prediction. The RL's parameters are continuously adjusted by the reward signal in BC. In this way, we could achieve a continuous motor state prediction when the neural patterns have drifted across time. The proposed algorithm is tested on a simulated rat lever-pressing experiment, where the rat's neural patterns have drifted across days. Compared with pure KF without re-calibration, our algorithm could follow the neural pattern drift in an online fashion and maintain good performance.Clinical Relevance- The proposed method bridges the gap between the online parameter adaptation and the continuous control of the neuro-prosthesis. It is promising to be used in adaptive brain control applications during clinical usage.},
}
@article {pmid34892618,
year = {2021},
author = {An, WW and Pei, A and Noyce, AL and Shinn-Cunningham, B},
title = {Decoding auditory attention from EEG using a convolutional neural network.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6586-6589},
doi = {10.1109/EMBC46164.2021.9630484},
pmid = {34892618},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Neural Networks, Computer ; Support Vector Machine ; },
abstract = {Brain-computer interface (BCI) systems allow users to communicate directly with a device using their brain. BCI devices leveraging electroencephalography (EEG) signals as a means of communication typically use manual feature engineering on the data to perform decoding. This approach is time intensive, requires substantial domain knowledge, and does not translate well, even to similar tasks. To combat this issue, we designed a convolutional neural network (CNN) model to perform decoding on EEG data collected from an auditory attention paradigm. Our CNN model not only bypasses the need for manual feature engineering, but additionally improves decoding accuracy (∼77%) and efficiency (∼11 bits/min) compared to a support vector machine (SVM) baseline. The results demonstrate the potential for the use of CNN in auditory BCI designs.},
}
@article {pmid34892617,
year = {2021},
author = {Alothman, A and Gilja, V},
title = {Unsupervised Channel Compression Methods in Motor Prostheses Design.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6581-6585},
doi = {10.1109/EMBC46164.2021.9630343},
pmid = {34892617},
issn = {2694-0604},
mesh = {*Artificial Limbs ; *Brain-Computer Interfaces ; *Data Compression ; Learning ; Neurons ; },
abstract = {The development of high performance brain machine interfaces (BMIs) requires scaling recording channel count to enable simultaneous recording from large populations of neurons. Unfortunately, proposed implantable neural interfaces have power requirements that scale linearly with channel count. To facilitate the design of interfaces with reduced power requirements, we propose and evaluate an unsupervised-learning-based compressed sensing strategy. This strategy suggests novel neural interface architectures which compress neural data by methodically combining channels of spiking activity. We develop an entropy-based compression strategy that models the population of neurons as being generated from a lower dimensional set of latent variables and aims to minimize the loss of information in the latent variables due to compression. We evaluate compressed features by inferring the latent variables from these features and measuring the accuracy with which the activity of held out neurons and arm movements can be estimated. We apply these methods to different cortical regions (PMd and M1) and compare the proposed compression methods to a random projections strategy often employed for compressed sensing and to a supervised regression based channel dropping strategy traditionally applied in BMI applications.},
}
@article {pmid34892608,
year = {2021},
author = {Dash, D and Ferrari, P and Babajani-Feremi, A and Borna, A and Schwindt, PDD and Wang, J},
title = {Magnetometers vs Gradiometers for Neural Speech Decoding.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6543-6546},
doi = {10.1109/EMBC46164.2021.9630489},
pmid = {34892608},
issn = {2694-0604},
support = {R03 DC013990/DC/NIDCD NIH HHS/United States ; R01 DC016621/DC/NIDCD NIH HHS/United States ; },
mesh = {Humans ; Magnetoencephalography ; Neuroimaging ; *Speech ; *Wearable Electronic Devices ; },
abstract = {Neural speech decoding aims at providing natural rate communication assistance to patients with locked-in state (e.g. due to amyotrophic lateral sclerosis, ALS) in contrast to the traditional brain-computer interface (BCI) spellers which are slow. Recent studies have shown that Magnetoencephalography (MEG) is a suitable neuroimaging modality to study neural speech decoding considering its excellent temporal resolution that can characterize the fast dynamics of speech. Gradiometers have been the preferred choice for sensor space analysis with MEG, due to their efficacy in noise suppression over magnetometers. However, recent development of optically pumped magnetometers (OPM) based wearable-MEG devices have shown great potential in future BCI applications, yet, no prior study has evaluated the performance of magnetometers in neural speech decoding. In this study, we decoded imagined and spoken speech from the MEG signals of seven healthy participants and compared the performance of magnetometers and gradiometers. Experimental results indicated that magnetometers also have the potential for neural speech decoding, although the performance was significantly lower than that obtained with gradiometers. Further, we implemented a wavelet based denoising strategy that improved the performance of both magnetometers and gradiometers significantly. These findings reconfirm that gradiometers are preferable in MEG based decoding analysis but also provide the possibility towards the use of magnetometers (or OPMs) for the development of the next-generation speech-BCIs.},
}
@article {pmid34892589,
year = {2021},
author = {Hosni, SMI and Borgheai, SB and McLinden, J and Zhu, S and Huang, X and Ostadabbas, S and Shahriari, Y},
title = {Graph-based Recurrence Quantification Analysis of EEG Spectral Dynamics for Motor Imagery-based BCIs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6453-6457},
doi = {10.1109/EMBC46164.2021.9630068},
pmid = {34892589},
issn = {2694-0604},
support = {P20 GM103430/GM/NIGMS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; Support Vector Machine ; },
abstract = {Despite continuous research, communication approaches based on brain-computer interfaces (BCIs) are not yet an efficient and reliable means that severely disabled patients can rely on. To date, most motor imagery (MI)-based BCI systems use conventional spectral analysis methods to extract discriminative features and classify the associated electroencephalogram (EEG)-based sensorimotor rhythms (SMR) dynamics that results in relatively low performance. In this study, we investigated the feasibility of using recurrence quantification analysis (RQA) and complex network theory graph-based feature extraction methods as a novel way to improve MI-BCIs performance. Rooted in chaos theory, these features explore the nonlinear dynamics underlying the MI neural responses as a new informative dimension in classifying MI.<br><br>METHOD: EEG time series recorded from six healthy participants performing MI-Rest tasks were projected into multidimensional phase space trajectories in order to construct the corresponding recurrence plots (RPs). Eight nonlinear graph-based RQA features were extracted from the RPs then compared to the classical spectral features through a 5-fold nested cross-validation procedure for parameter optimization using a linear support vector machine (SVM) classifier.<br><br>RESULTS: Nonlinear graph-based RQA features were able to improve the average performance of MI-BCI by 5.8% as compared to the classical features.<br><br>SIGNIFICANCE: These findings suggest that RQA and complex network analysis could represent new informative dimensions for nonlinear characteristics of EEG signals in order to enhance the MI-BCI performance.},
}
@article {pmid34892587,
year = {2021},
author = {Zhang, Y and Wan, Z and Wan, G and Zheng, Q and Chen, W and Zhang, S},
title = {Changes in Modulation Characteristics of Neurons in Different Modes of Motion Control Using Brain-Machine Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6445-6448},
doi = {10.1109/EMBC46164.2021.9630212},
pmid = {34892587},
issn = {2694-0604},
mesh = {Animals ; *Brain-Computer Interfaces ; Macaca mulatta ; Motion ; Neurons ; },
abstract = {In the research of motion control using brain-machine interface (BMI), analysis is usually conducted on one ensemble of neurons whose activity serves as direct input to the BMI decoder (control units). The number of control units is diverse in different control modes. That is to say, the size of dimensions of neural signals used in motion control is diverse. However, how will the behavioral performance change with this kind of diversity? What effects does this diversity have on modulation characteristics of control units? To answer these questions, we designed three modes of motion tasks using neural signals with different dimension sizes to control. Our results imply that as the dimension reduces, some deviations appear in behavioral performance. At the same time, the control units tend to have a directional division of control, then enhance their stability and increase modulations after division.},
}
@article {pmid34892582,
year = {2021},
author = {Krana, M and Farmaki, C and Pediaditis, M and Sakkalis, V},
title = {SSVEP based Wheelchair Navigation in Outdoor Environments.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6424-6427},
doi = {10.1109/EMBC46164.2021.9629516},
pmid = {34892582},
issn = {2694-0604},
mesh = {Electroencephalography ; Evoked Potentials ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; *Wheelchairs ; },
abstract = {A promising application of Brain Computer Interfaces (BCIs), and in particular of Steady-State Visually Evoked Potentials (SSVEP) is wheelchair navigation which can facilitate the daily life of patients suffering from severe paralysis. However, the outdoor performance of such a system is highly affected by uncontrolled environmental factors. In this paper, we present an SSVEP-based wheelchair navigation system and propose incremental learning as a method of adapting the system to changing environmental conditions.},
}
@article {pmid34892577,
year = {2021},
author = {Liu, C and Li, M and Wang, R and Cui, X and Jung, H and Halin, K and You, H and Yang, X and Chen, W},
title = {Online Decoding System with Calcium Image From Mice Primary Motor Cortex.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6402-6405},
doi = {10.1109/EMBC46164.2021.9630138},
pmid = {34892577},
issn = {2694-0604},
mesh = {Animals ; *Brain-Computer Interfaces ; Calcium ; Mice ; *Motor Cortex ; Online Systems ; Signal Processing, Computer-Assisted ; },
abstract = {With the development of calcium imaging, neuroscientists have been able to study neural activity with a higher spatial resolution. However, the real-time processing of calcium imaging is still a big challenge for future experiments and applications. Most neuroscientists have to process their imaging data offline due to the time-consuming of most existing calcium imaging analysis methods. We proposed a novel online neural signal processing framework for calcium imaging and established an Optical Brain-Computer Interface System (OBCIs) for decoding neural signals in real-time. We tested and evaluated this system by classifying the calcium signals obtained from the primary motor cortex of mice when the mice were performing a lever-pressing task. The performance of our online system could achieve above 80% in the average decoding accuracy. Our preliminary results show that the online neural processing framework could be applied to future closed-loop OBCIs studies.},
}
@article {pmid34892563,
year = {2021},
author = {Chen, S and Liu, X and Wang, Y},
title = {Considering Neural Connectivity in Point Process Decoder for Brain-Machine Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6341-6344},
doi = {10.1109/EMBC46164.2021.9630383},
pmid = {34892563},
issn = {2694-0604},
mesh = {Action Potentials ; Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Neurons ; },
abstract = {Brain machine interface (BMI) can translate neural activity into digital commands to control prostheses. The decoder in BMI models the mechanism relating to neural activity and intents in brain. In our brain, single neuronal tuning property and neural connectivity contribute to encoding the intents together. These properties may change, a phenomenon which is named neural adaptation during using BMIs. Neural adaptation requires the decoder to consider the two factors at the same time and has the potential to follow their changes. However, in the previous work, the class of neural network and clustering decoder can consider the neural connectivity regardless of the single neuronal tuning property. On the other hand, point process methods can model the single neuronal tuning property but fail to address the neural connectivity. In this paper, we propose a new point process decoder with the information of neural connectivity named NCPP. We derive the neural connectivity component from the point process method by Bayes' rule and use a clustering decoder to represent the neural connectivity. This method can consider the neural connectivity and the single neuronal tuning property at the same time. We validate the method on simulation data where the point process method cannot achieve a good decoding performance and compare it with sequential Monte Carlo point process method (SMCPP). The results show our method outperforms the pure point process method which indicates our method can model the neural connectivity and single neuronal tuning property at the same time.Clinical Relevance-This paper proposes a decoder that can model the neural connectivity and the single neuronal tuning property at the same time, which is potential to explain the neural adaptation computationally.},
}
@article {pmid34892562,
year = {2021},
author = {Nagarajan, A and Robinson, N and Guan, C},
title = {Investigation on Robustness of EEG-based Brain-Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6334-6340},
doi = {10.1109/EMBC46164.2021.9630031},
pmid = {34892562},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Machine Learning ; },
abstract = {Electroencephalogram (EEG)-based brain-computer interface (BCI) systems tend to suffer from performance degradation due to the presence of noise and artifacts in EEG data. This study is aimed at systematically investigating the robustness of state-of-the-art machine learning and deep learning based EEG-BCI models for motor imagery classification against simulated channel-specific noise in EEG data, at various low values of signal-to-noise ratio (SNR). Our results illustrate higher robustness of deep learning based MI classification models compared to the traditional machine learning based model, while identifying a set of channels with large sensitivity to simulated channel-specific noise. The EEGNet is relatively more robust towards channel-specific noise than Shallow ConvNet and FBCSP. We propose a preliminary solution, based on activation function, to improve the robustness of the deep learning models. By using saturating nonlinearities, the percentage drop in classification accuracy for SNR of -18 dB had reduced from 10.99% to 6.53% for EEGNet and 14.05% to 3.57% for Shallow ConvNet. Through this study, we emphasize the need for a more precise solution for enhancing the robustness, and thereby usability of EEG-BCI systems.},
}
@article {pmid34892533,
year = {2021},
author = {Sahoo, KP and Radhakrishnan, A and Pratiher, S and Alam, S and Kerick, S and Ghosh, N and Chhan, D and Banerjee, N and Patra, A},
title = {Alterations in Multi-channel EEG Dynamics During a Stressful Shooting Task in Virtual Reality Systems.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6207-6210},
doi = {10.1109/EMBC46164.2021.9630007},
pmid = {34892533},
issn = {2694-0604},
mesh = {Brain ; *Electroencephalography ; User-Computer Interface ; *Virtual Reality ; },
abstract = {This paper explores power spectrum-based features extracted from the 64-channel electroencephalogram (EEG) signals to analyze brain activity alterations during a virtual reality (VR)-based stressful shooting task, with low and high difficulty levels, from an initial resting baseline. This paper also investigates the variations in EEG across several experimental sessions performed over multiple days. Results indicate that patterns of changes in different power bands of the EEG are consistent with high mental stress levels during the shooting task compared to baseline. Although there is one inconsistency, overall, the brain patterns indicate higher stress levels during high difficulty tasks than low difficulty tasks and in the first session compared to the last session.},
}
@article {pmid34892532,
year = {2021},
author = {Meyer, SM and Rao Mangalore, A and Ehrlich, SK and Berberich, N and Nassour, J and Cheng, G},
title = {A Comparative Pilot Study on ErrPs for Different Usage Conditions of an Exoskeleton with a Mobile EEG Device.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6203-6206},
doi = {10.1109/EMBC46164.2021.9630465},
pmid = {34892532},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; *Exoskeleton Device ; Pilot Projects ; },
abstract = {Exoskeletons and prosthetic devices controlled using brain-computer interfaces (BCIs) can be prone to errors due to inconsistent decoding. In recent years, it has been demonstrated that error-related potentials (ErrPs) can be used as a feedback signal in electroencephalography (EEG) based BCIs. However, modern BCIs often take large setup times and are physically restrictive, making them impractical for everyday use. In this paper, we use a mobile and easy-to-setup EEG device to investigate whether an erroneously functioning 1-DOF exoskeleton in different conditions, namely, visually observing and wearing the exoskeleton, elicits a brain response that can be classified. We develop a pipeline that can be applied to these two conditions and observe from our experiments that there is evidence for neural responses from electrodes near regions associated with ErrPs in an environment that resembles the real world. We found that these error-related responses can be classified as ErrPs with accuracies ranging from 60% to 71%, depending on the condition and the subject. Our pipeline could be further extended to detect and correct erroneous exoskeleton behavior in real-world settings.},
}
@article {pmid34892528,
year = {2021},
author = {Chin, ZY and Zhang, Z and Wang, C and Ang, KK},
title = {An Affective Interaction System using Virtual Reality and Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6183-6186},
doi = {10.1109/EMBC46164.2021.9630045},
pmid = {34892528},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Support Vector Machine ; *Virtual Reality ; },
abstract = {Affective Computing is a multidisciplinary area of research that allows computers to perform human emotion recognition, with potential applications in areas such as healthcare, gaming and intuitive human computer interface design. Hence, this paper proposes an affective interaction system using dry EEG-based Brain-Computer Interface and Virtual Reality (BCI-VR). The proposed BCI-VR system integrates existing low-cost consumer devices such as an EEG headband with frontal and temporal dry electrodes for brain signal acquisition, and a low-cost VR headset that houses an Android handphone. The handphone executes an in-house developed software that connects wirelessly to the headband, processes the acquired EEG signals, and displays VR content to elicit emotional responses. The proposed BCI-VR system was used to collect EEG data from 13 subjects while they watched VR content that elicits positive or negative emotional responses. EEG bandpower features were extracted to train Linear Discriminant and Support Vector Machine classifiers. The classification performances of these classifiers on this dataset and the results of a public dataset (SEED-IV) are then evaluated. The results in classifying positive vs negative emotions in both datasets (~66% for 2-class) show promise that positive and negative emotions can be detected by the proposed low cost BCIVR system, yielding nearly the same performance on the public dataset that used wet EEG electrodes. Hence the results show promise of the proposed BCI-VR system for real-time affective interaction applications in future.},
}
@article {pmid34892524,
year = {2021},
author = {Nasrollahpour, M and Zaeimbashi, M and Khalifa, A and Liang, X and Chen, H and Sun, N and Abrishami, SMS and Martos-Repath, I and Emam, S and Cash, S and Sun, NX},
title = {Magnetoelectric (ME) Antenna for On-chip Implantable Energy Harvesting.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6167-6170},
doi = {10.1109/EMBC46164.2021.9629823},
pmid = {34892524},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Prostheses and Implants ; *Wireless Technology ; },
abstract = {A novel magnetoelectric (ME) antenna is fabricated to be integrated to the on-chip energy harvesting circuit for brain-computer interface applications. The proposed ME antenna resonates at the frequency of 2.57 GHz while providing a bandwidth of 3.37 MHz. The proposed rectangular ME antenna wireless power transfer efficiency is 0.304 %, which is considerably higher than that of micro-coils.Clinical Relevance- This provides a suitable energy harvesting efficiency for wirelessly powering up the brain implant devices.},
}
@article {pmid34892520,
year = {2021},
author = {Mu, J and Tan, Y and Grayden, DB and Oetomo, D},
title = {Multi-Frequency Canonical Correlation Analysis (MFCCA): A Generalised Decoding Algorithm for Multi-Frequency SSVEP.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6151-6154},
doi = {10.1109/EMBC46164.2021.9629669},
pmid = {34892520},
issn = {2694-0604},
mesh = {Algorithms ; Canonical Correlation Analysis ; *Electroencephalography ; *Evoked Potentials, Visual ; Photic Stimulation ; },
abstract = {Stimulation methods that utilise more than one stimulation frequency have been developed for steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) with the purpose of increasing the number of targets that can be presented simultaneously. However, there is no unified decoding algorithm that can be used without training for each individual users or cases, and applied to a large class of multi-frequency stimulated SSVEP settings. This paper extends the widely used canonical correlation analysis (CCA) decoder to explicitly accommodate multi-frequency SSVEP by exploiting the interactions between the multiple stimulation frequencies. A concept of order, defined as the sum of absolute value of the coefficients in the linear combination of the input frequencies, was introduced to assist the design of Multi-Frequency CCA (MFCCA). The probability distribution of the order in the resulting SSVEP response was then used to improve decoding accuracy. Results show that, compared to the standard CCA formulation, the proposed MFCCA has a 20% improvement in decoding accuracy on average at order 2, while keeping its generality and training-free characteristics.},
}
@article {pmid34892518,
year = {2021},
author = {Zhang, Z and Guan, K and Wang, L and Chai, X and Ma, Y and Gao, X and Liu, T and Niu, H},
title = {Effects of Jaw Clench Actions on Steady-State Visual Evoked Potential Detection at Some Typical Frequencies.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6142-6145},
doi = {10.1109/EMBC46164.2021.9629729},
pmid = {34892518},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Canonical Correlation Analysis ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {More and more hybrid brain-computer interfaces (BCI) supplement traditional single-modality BCI in practical applications. Combinations based on steady-state visual evoked potential (SSVEP) and electromyography (EMG) are the widely used hybrid BCIs. The EMG of jaw clench is commonly used together with SSVEP. This article explored the interference with SSVEP from occipital electrodes by the jaw clench-related EMG so that SSVEP with specific frequency can be identified even during occlusal movements. The experiment was divided into three sets base on the jaw clench patterns (no clenches, chew, and long clench). In each set, the subjects used the same visual stimuli, which were realized by the three flashing targets at different frequencies (6.2Hz, 9.8Hz, and 14.6Hz). After collecting the SSVEP at 4 sites in the occipital region, the SSVEP response spectrum of each stimulus was observed under the three jaw clench patterns. Then, the SSVEP signal was identified by the canonical correlation analysis method for accuracy statistics. Spectrum responses showed that the interference of the jaw clench EMG on SSVEP could be avoided when the stimulation frequency is lower than 20Hz. SSVEP could be identified based on the frequency domain characteristics of these signals. During steady-state visual stimulation with jaw clenches, the recognition rate of SSVEP was still high (no clenches: 100.0%, chew: 94.7%, and long clench: 100.0%). Through reasonable frequency selecting and signal processing, the influence of the jaw clench movement on the SSVEP could be reduced and a high recognition accuracy could be achieved, even the jaw clench actions and the SSVEP stimulation occur simultaneously.},
}
@article {pmid34892512,
year = {2021},
author = {Jia, T and Mo, L and Li, C and Liu, A and Li, Z and Ji, L},
title = {5 Hz rTMS improves motor-imagery based BCI classification performance.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6116-6120},
doi = {10.1109/EMBC46164.2021.9630102},
pmid = {34892512},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; *Transcranial Magnetic Stimulation ; },
abstract = {Brain-computer interface (BCI) based rehabilitation has been proven a promising method facilitating motor recovery. Recognizing motor intention is crucial for realizing BCI rehabilitation training. Event-related desynchronization (ERD) is a kind of electroencephalogram (EEG) inherent characteristics associated with motor intention. However, due to brain deficits poststroke, some patients are not able to generate ERD, which discourages them to be involved in BCI rehabilitation training. To boost ERD during motor imagery (MI), this paper investigates the effects of high-frequency repetitive transcranial magnetic stimulation (rTMS) on BCI classification performance. Eleven subjects participated in this study. The experiment consisted of two conditions: rTMS + MI versus sham rTMS + MI, which were arranged on different days. MI tests with 64-channel EEG recording were arranged immediately before and after rTMS and sham rTMS. Time-frequency analysis were utilized to measure ERD changes. Common spatial pattern was used to extract features and linear discriminant analysis was used to calculate offline classification accuracies. Paired-sample t-test and Wilcoxon signed rank tests with post-hoc analysis were used to compare performance before and after stimulation. Statistically stronger ERD (-13.93±12.99%) was found after real rTMS compared with ERD (-5.71±21.25%) before real rTMS (p<0.05). Classification accuracy after real rTMS (70.71±10.32%) tended to be higher than that before real rTMS (66.50±8.48%) (p<0.1). However, no statistical differences were found after sham stimulation. This research provides an effective method in improving BCI performance by utilizing neural modulation.Clinical Relevance- This study offers a promising treatment for patients who cannot be recruited in BCI rehabilitation training due to poor BCI classification performance.},
}
@article {pmid34892509,
year = {2021},
author = {Pals, M and Belizon, RJP and Berberich, N and Ehrlich, SK and Nassour, J and Cheng, G},
title = {Demonstrating the Viability of Mapping Deep Learning Based EEG Decoders to Spiking Networks on Low-powered Neuromorphic Chips.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6102-6105},
doi = {10.1109/EMBC46164.2021.9629621},
pmid = {34892509},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Neural Networks, Computer ; },
abstract = {Accurate and low-power decoding of brain signals such as electroencephalography (EEG) is key to constructing brain-computer interface (BCI) based wearable devices. While deep learning approaches have progressed substantially in terms of decoding accuracy, their power consumption is relatively high for mobile applications. Neuromorphic hardware arises as a promising solution to tackle this problem since it can run massive spiking neural networks with energy consumption orders of magnitude lower than traditional hardware. Herein, we show the viability of directly mapping a continuous-valued convolutional neural network for motor imagery EEG classification to a spiking neural network. The converted network, able to run on the SpiNNaker neuromorphic chip, only shows a 1.91% decrease in accuracy after conversion. Thus, we take full advantage of the benefits of both deep learning accuracies and low-power neuro-inspired hardware, properties that are key for the development of wearable BCI devices.},
}
@article {pmid34892508,
year = {2021},
author = {Ottenhoff, MC and Goulis, S and Wagner, L and Tousseyn, S and Colon, A and Kubben, P and Herff, C},
title = {Continuously Decoding Grasping Movements using Stereotactic Depth Electrodes.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6098-6101},
doi = {10.1109/EMBC46164.2021.9629639},
pmid = {34892508},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electrodes ; *Electroencephalography ; Hand Strength ; Humans ; Movement ; },
abstract = {Brain-Computer Interfaces (BCIs) that decode a patient's movement intention to control a prosthetic device could restore some independence to paralyzed patients. An important step on the road towards naturalistic prosthetic control is to decode movement continuously with low-latency. BCIs based on intracortical micro-arrays provide continuous control of robotic arms, but require a minor craniotomy. Surface recordings of neural activity using EEG have made great advances over the last years, but suffer from high noise levels and large intra-session variance. Here, we investigate the use of minimally invasive recordings using stereotactically implanted EEG (sEEG). These electrodes provide a sparse sampling across many brain regions. So far, promising decoding results have been presented using data measured from the subthalamic nucleus or trial-to-trial based methods using depth electrodes. In this work, we demonstrate that grasping movements can continuously be decoded using sEEG electrodes, as well. Beta and high-gamma activity was extracted from eight participants performing a grasping task. We demonstrate above chance level decoding of movement vs rest and left vs right, from both frequency bands with accuracies up to 0.94 AUC. The vastly different electrode locations between participants lead to large variability. In the future, we hope that sEEG recordings will provide additional information for the decoding process in neuroprostheses.},
}
@article {pmid34892505,
year = {2021},
author = {Sato, H and Yoshida, A and Shimada, T and Fukami, T},
title = {Performance Improvement of EEG-Based BCI Using Visual Feedback Based on Evaluation Scores Calculated by a Computer.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6086-6089},
doi = {10.1109/EMBC46164.2021.9630801},
pmid = {34892505},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Computers ; Electroencephalography ; Feedback, Sensory ; Humans ; },
abstract = {In the study of an electroencephalography (EEG)-based brain computer interface (BCI) using the P300, there have been many reports on computer algorithms that identify the target intended by a user from multiple candidates. However, because the P300 amplitude depends on the subject's condition and is attenuated by physical and mental factors, such as fatigue and motivation, the performance of the BCI is low. Therefore, we aim to improve performance by introducing a feedback mechanism that provides the user with an evaluation calculated by the computer during EEG measurement. In this study, we conducted an experiment in which the user input one character from four characters on the display. By changing the character size according to the evaluation score calculated by the computer, the computer's current evaluation was fed back to the user. This is expected to change the consciousness of the user to enable them to execute a task by knowing the evaluation; that is, if the evaluation is high, the user needs to maintain the current state, and if the evaluation is low, a behavioral change, such as increasing attention, is required to improve the evaluation.As a result of comparing 10 subjects with and without feedback, accuracy improved for seven subjects that were given feedback.},
}
@article {pmid34892495,
year = {2021},
author = {Angrick, M and Ottenhoff, M and Goulis, S and Colon, AJ and Wagner, L and Krusienski, DJ and Kubben, PL and Schultz, T and Herff, C},
title = {Speech Synthesis from Stereotactic EEG using an Electrode Shaft Dependent Multi-Input Convolutional Neural Network Approach.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6045-6048},
doi = {10.1109/EMBC46164.2021.9629711},
pmid = {34892495},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electrocorticography ; Electrodes, Implanted ; Humans ; Neural Networks, Computer ; *Speech ; },
abstract = {Neurological disorders can lead to significant impairments in speech communication and, in severe cases, cause the complete loss of the ability to speak. Brain-Computer Interfaces have shown promise as an alternative communication modality by directly transforming neural activity of speech processes into a textual or audible representations. Previous studies investigating such speech neuroprostheses relied on electrocorticography (ECoG) or microelectrode arrays that acquire neural signals from superficial areas on the cortex. While both measurement methods have demonstrated successful speech decoding, they do not capture activity from deeper brain structures and this activity has therefore not been harnessed for speech-related BCIs. In this study, we bridge this gap by adapting a previously presented decoding pipeline for speech synthesis based on ECoG signals to implanted depth electrodes (sEEG). For this purpose, we propose a multi-input convolutional neural network that extracts speech-related activity separately for each electrode shaft and estimates spectral coefficients to reconstruct an audible waveform. We evaluate our approach on open-loop data from 5 patients who conducted a recitation task of Dutch utterances. We achieve correlations of up to 0.80 between original and reconstructed speech spectrograms, which are significantly above chance level for all patients (p < 0.001). Our results indicate that sEEG can yield similar speech decoding performance to prior ECoG studies and is a promising modality for speech BCIs.},
}
@article {pmid34892491,
year = {2021},
author = {Zhang, Y and Zhang, L and Wang, G and Lyu, W and Ran, Y and Su, S and Xu, P and Yao, D},
title = {Noise-assisted Multivariate Empirical Mode Decomposition based Causal Decomposition for Detecting Upper Limb Movement in EEG-EMG Hybrid Brain Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6029-6032},
doi = {10.1109/EMBC46164.2021.9630384},
pmid = {34892491},
issn = {2694-0604},
mesh = {Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Signal Processing, Computer-Assisted ; Upper Extremity ; },
abstract = {EEG-EMG based hybrid Brain Computer Interface (hBCI) utilizes the brain-muscle physiological system to interpret and identify motor behaviors, and transmit human intelligence to automated machines in AI applications such as neurorehabilitations and brain-like intelligence. The study introduces a hBCI method for motor behaviors, where multiple time series of the brain neuromuscular network are introduced to indicate brain-muscle causal interactions, and features are extracted based on Relative Causal Strengths (RCSs) derived by Noise-assisted Multivariate Empirical Mode Decomposition (NA-MEMD) based Causal Decomposition. The complex process in brain neuromuscular interactions is specifically investigated towards a monitoring task of upper limb movement, whose 63-channel EEGs and 2-channel EMGs are composed of data inputs. The energy and frequency factors counted from RCSs were extracted as Core Features (CFs). Results showed accuracies of 91.4% and 81.4% with CFs for identifying cascaded (No Movement and Movement Execution) and 3-class (No Movement, Right Movement, and Left Movement) using Naive Bayes classifier, respectively. Moreover, those reached 100% and 94.3% when employing CFs combined with eigenvalues processed by Common Spatial Pattern (CSP). This initial work implies a novel causality inference based hBCI solution for the detection of human upper limb movement.},
}
@article {pmid34892481,
year = {2021},
author = {Alfeo, AL and Catrambone, V and Cimino, MGCA and Vaglini, G and Valenza, G},
title = {Recognizing motor imagery tasks from EEG oscillations through a novel ensemble-based neural network architecture.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5983-5986},
doi = {10.1109/EMBC46164.2021.9629900},
pmid = {34892481},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Neural Networks, Computer ; },
abstract = {Brain-Computer Interfaces (BCI) provide effective tools aimed at recognizing different brain activities, translate them into actions, and enable humans to directly communicate through them. In this context, the need for strong recognition performances results in increasingly sophisticated machine learning (ML) techniques, which may result in poor performance in a real application (e.g., limiting a real-time implementation). Here, we propose an ensemble approach to effectively balance between ML performance and computational costs in a BCI framework. The proposed model builds a classifier by combining different ML models (base-models) that are specialized to different classification sub-problems. More specifically, we employ this strategy with an ensemble-based architecture consisting of multi-layer perceptrons, and test its performance on a publicly available electroencephalography-based BCI dataset with four-class motor imagery tasks. Compared to previously proposed models tested on the same dataset, the proposed approach provides greater average classification performances and lower inter-subject variability.},
}
@article {pmid34892479,
year = {2021},
author = {Wang, K and Qiu, S and Wei, W and Zhang, C and He, H and Xu, M and Ming, D},
title = {Vigilance Estimating in SSVEP-Based BCI Using Multimodal Signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5974-5978},
doi = {10.1109/EMBC46164.2021.9629736},
pmid = {34892479},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Electrooculography ; *Evoked Potentials, Visual ; Humans ; },
abstract = {Brain-computer interface (BCI) is a communication system that allows a direct connection between the human brain and external devices. With the application of BCI, it is important to estimate vigilance for BCI users. In order to investigate the vigilance changes of the subjects during BCI tasks and develop a multimodal method to estimate the vigilance level, a high-speed 4-target BCI system for cursor control was built based on steady-state visual evoked potential (SSVEP). 18 participants were recruited and underwent a 90-min continuous cursor-control BCI task, when electroencephalogram (EEG), electrooculogram (EOG), electrocardiography (ECG), and electrodermal activity (EDA) were recorded simultaneously. Then, we extracted features from the multimodal signals and applied regression models to estimate vigilance. Experimental results showed that the differential entropy (DE) feature could effectively reflect the change of vigilance. The vigilance estimation method, which integrates DE and EOG features into the support vector regression (SVR) model, achieved a better performance than the compared methods. These results demonstrate the feasibility of our methods for estimating vigilance levels in BCI.},
}
@article {pmid34892469,
year = {2021},
author = {Nur Chowdhury, MS and Dutta, A and Robison, MK and Blais, C and Brewer, G and Bliss, DW},
title = {3D CNN to Estimate Reaction Time from Multi-Channel EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5932-5935},
doi = {10.1109/EMBC46164.2021.9630748},
pmid = {34892469},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Machine Learning ; Neural Networks, Computer ; Reaction Time ; },
abstract = {The study of human reaction time (RT) is invaluable not only to understand the sensory-motor functions but also to translate brain signals into machine comprehensible commands that can facilitate augmentative and alternative communication using brain-computer interfaces (BCI). Recent developments in sensor technologies, hardware computational capabilities, and neural network models have significantly helped advance biomedical signal processing research. This study is an attempt to utilize state-of-the-art resources to explore the relationship between human behavioral responses during perceptual decision-making and corresponding brain signals in the form of electroencephalograms (EEG). In this paper, a generalized 3D convolutional neural network (CNN) architecture is introduced to estimate RT for a simple visual task using single-trial multi-channel EEG. Earlier comparable studies have also employed a number of machine learning and deep learning-based models, but none of them considered inter-channel relationships while estimating RT. On the contrary, the use of 3D convolutional layers enabled us to consider the spatial relationship among adjacent channels while simultaneously utilizing spectral information from individual channels. Our model can predict RT with a root mean square error of 91.5 ms and a correlation coefficient of 0.83. These results surpass all the previous results attained from different studies.Clinical relevance Novel approaches to decode brain signals can facilitate research on brain-computer interfaces (BCIs), psychology, and neuroscience, enabling people to utilize assistive devices by root-causing psychological or neuromuscular disorders.},
}
@article {pmid34892467,
year = {2021},
author = {Mu, J and Grayden, DB and Tan, Y and Oetomo, D},
title = {Frequency Superposition - A Multi-Frequency Stimulation Method in SSVEP-based BCIs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5924-5927},
doi = {10.1109/EMBC46164.2021.9630511},
pmid = {34892467},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Canonical Correlation Analysis ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {The steady-state visual evoked potential (SSVEP) is one of the most widely used modalities in brain-computer interfaces (BCIs) due to its many advantages. However, the existence of harmonics and the limited range of responsive frequencies in SSVEP make it challenging to further expand the number of targets without sacrificing other aspects of the interface or putting additional constraints on the system. This paper introduces a novel multi-frequency stimulation method for SSVEP and investigates its potential to effectively and efficiently increase the number of targets presented. The proposed stimulation method, obtained by the superposition of the stimulation signals at different frequencies, is size-efficient, allows single-step target identification, puts no strict constraints on the usable frequency range, can be suited to self-paced BCIs, and does not require specific light sources. In addition to the stimulus frequencies and their harmonics, the evoked SSVEP waveforms include frequencies that are integer linear combinations of the stimulus frequencies. Results of decoding SSVEPs collected from nine subjects using canonical correlation analysis (CCA) with only the frequencies and harmonics as reference, also demonstrate the potential of using such a stimulation paradigm in SSVEP-based BCIs.},
}
@article {pmid34892464,
year = {2021},
author = {Kobler, RJ and Hirayama, JI and Hehenberger, L and Lopes-Dias, C and Muller-Putz, GR and Kawanabe, M},
title = {On the interpretation of linear Riemannian tangent space model parameters in M/EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5909-5913},
doi = {10.1109/EMBC46164.2021.9630144},
pmid = {34892464},
issn = {2694-0604},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Magnetoencephalography ; Space Simulation ; },
abstract = {Riemannian tangent space methods offer state-of-the-art performance in magnetoencephalography (MEG) and electroencephalography (EEG) based applications such as brain-computer interfaces and biomarker development. One limitation, particularly relevant for biomarker development, is limited model interpretability compared to established component-based methods. Here, we propose a method to transform the parameters of linear tangent space models into interpretable patterns. Using typical assumptions, we show that this approach identifies the true patterns of latent sources, encoding a target signal. In simulations and two real MEG and EEG datasets, we demonstrate the validity of the proposed approach and investigate its behavior when the model assumptions are violated. Our results confirm that Riemannian tangent space methods are robust to differences in the source patterns across observations. We found that this robustness property also transfers to the associated patterns.},
}
@article {pmid34892463,
year = {2021},
author = {Tang, Y and Zhang, JJ and Corballis, PM and Hallum, LE},
title = {Towards the Classification of Error-Related Potentials using Riemannian Geometry.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5905-5908},
doi = {10.1109/EMBC46164.2021.9629583},
pmid = {34892463},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Feedback ; Humans ; },
abstract = {The error-related potential (ErrP) is an event-related potential (ERP) evoked by an experimental participant's recognition of an error during task performance. ErrPs, originally described by cognitive psychologists, have been adopted for use in brain-computer interfaces (BCIs) for the detection and correction of errors, and the online refinement of decoding algorithms. Riemannian geometry-based feature extraction and classification is a new approach to BCI which shows good performance in a range of experimental paradigms, but has yet to be applied to the classification of ErrPs. Here, we describe an experiment that elicited ErrPs in seven normal participants performing a visual discrimination task. Audio feedback was provided on each trial. We used multi-channel electroencephalogram (EEG) recordings to classify ErrPs (success/failure), comparing a Riemannian geometry-based method to a traditional approach that computes time-point features. Overall, the Riemannian approach outperformed the traditional approach (78.2% versus 75.9% accuracy, p <0.05); this difference was statistically significant (p <0.05) in three of seven participants. These results indicate that the Riemannian approach better captured the features from feedback-elicited ErrPs, and may have application in BCI for error detection and correction.},
}
@article {pmid34892460,
year = {2021},
author = {Mussabayeva, A and Jamwal, PK and Tahir Akhtar, M},
title = {Ensemble Learning Approach for Subject-Independent P300 Speller.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5893-5896},
doi = {10.1109/EMBC46164.2021.9629679},
pmid = {34892460},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Event-Related Potentials, P300 ; Humans ; Support Vector Machine ; },
abstract = {P300 speller is a brain-computer interface (BCI) speller system, used for enabling human with different paralyzing disorders, such as amyotrophic lateral sclerosis (ALS), to communicate with the outer world by processing electroencephalography (EEG) signals. Different people have different latency and amplitude of the P300 event-related potential (ERP) component, which is used as the main feature for detecting the target character. In order to achieve robust results for different subjects using generic training (GT), the ensemble learning classifiers are proposed based on linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbors (kNN), and convolutional neural network (CNN). The proposed models are trained using data from healthy subjects and tested on both healthy subjects and ALS patients. The results show that the fusion of LDA, kNN and SVM provides the most accurate results, achieving the accuracy of 99% for healthy subjects and about 85% for ALS patients.},
}
@article {pmid34892456,
year = {2021},
author = {Venot, T and Corsi, MC and Saint-Bauzel, L and Vico Fallani, F},
title = {Towards multimodal BCIs: the impact of peripheral control on motor cortex activity and sense of agency.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5876-5879},
doi = {10.1109/EMBC46164.2021.9630021},
pmid = {34892456},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; *Motor Cortex ; Reproducibility of Results ; },
abstract = {In the recent years, brain computer interfaces (BCI) using motor imagery have shown some limitations regarding the quality of control. In an effort to improve this promising technology, some studies intended to develop hybrid BCI with other technologies such as eye tracking which shows more reliability. However, the use of an eye tracker in the control of a robot might affect by itself the sense of agency (SoA) and the brain activity in the regions used for motor imagery (MI). Here, we explore the link between the sense of agency and the activity of the motor cortex. For this purpose, we used of a virtual arm projected on a surface which is either controlled by motion capture or controlled by gaze using an eye tracker. We found out that there is an activity in the motor cortex during the task of control by gaze and that having control over a projected robotic arm presents significant differences with the situation of observing the robot moving.},
}
@article {pmid34892453,
year = {2021},
author = {Floreani, ED and Rowley, D and Khan, N and Kelly, D and Robu, I and Kirton, A and Kinney-Lang, E},
title = {Unlocking Independence: Exploring Movement with Brain-Computer Interface for Children with Severe Physical Disabilities.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5864-5867},
doi = {10.1109/EMBC46164.2021.9630578},
pmid = {34892453},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Child ; Electroencephalography ; Humans ; Movement ; Pilot Projects ; *Self-Help Devices ; },
abstract = {Children with severe physical disabilities are often unable to independently explore their environments, further contributing to complex developmental delays. Brain-computer interfaces (BCIs) could be a novel access method to power mobility for children who struggle to use existing alternate access technologies, allowing them to reap the developmental, social, and psychological benefits of independent mobility. In this pilot study we demonstrated that children with quadriplegic cerebral palsy can use a simple BCI system to explore movement with a power mobility device. Four children were able to use the BCI to drive forward at least 7m, although more practice is needed to achieve more efficient driving skills through sustained BCI activations.},
}
@article {pmid34892452,
year = {2021},
author = {Lu, HY and Bollimunta, A and Eaton, RW and Morrison, JH and Moxon, KA and Carmena, JM and Nassi, JJ and Santacruz, SR},
title = {Short-training Algorithm for Online Brain-machine Interfaces Using One-photon Microendoscopic Calcium Imaging.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5860-5863},
doi = {10.1109/EMBC46164.2021.9629838},
pmid = {34892452},
issn = {2694-0604},
mesh = {Algorithms ; Artifacts ; *Brain-Computer Interfaces ; Calcium ; Photons ; },
abstract = {Calcium imaging has great potential to be applied to online brain-machine interfaces (BMIs). As opposed to two-photon imaging settings, a one-photon microendoscopic imaging device can be chronically implanted and is subject to little motion artifacts. Traditionally, one-photon microendoscopic calcium imaging data are processed using the constrained nonnegative matrix factorization (CNMFe) algorithm, but this batched processing algorithm cannot be applied in real-time. An online analysis of calcium imaging data algorithm (or OnACIDe) has been proposed, but OnACIDe updates the neural components by repeatedly performing neuron identification frame-by-frame, which may decelerate the update speed if applying to online BMIs. For BMI applications, the ability to track a stable population of neurons in real-time has a higher priority over accurately identifying all the neurons in the field of view. By leveraging the fact that 1) microendoscopic recordings are rather stable with little motion artifacts and 2) the number of neurons identified in a short training period is sufficient for potential online BMI tasks such as cursor movements, we proposed the short-training CNMFe algorithm (stCNMFe) that skips motion correction and neuron identification processes to enable a more efficient BMI training program in a one-photon microendoscopic setting.},
}
@article {pmid34892450,
year = {2021},
author = {Marjaninejad, A and Klaes, C and Valero-Cuevas, FJ},
title = {Data-efficient Causal Decoding of Spiking Neural Activity using Weighted Voting.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5850-5855},
doi = {10.1109/EMBC46164.2021.9631022},
pmid = {34892450},
issn = {2694-0604},
support = {R21 NS113613/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Humans ; Parietal Lobe ; Politics ; },
abstract = {Brain-Computer Interface systems can contribute to a vast set of applications such as overcoming physical disabilities in people with neural injuries or hands-free control of devices in healthy individuals. However, having systems that can accurately interpret intention online remains a challenge in this field. Robust and data-efficient decoding-despite the dynamical nature of cortical activity and causality requirements for physical function-is among the most important challenges that limit the widespread use of these devices for real-world applications. Here, we present a causal, data-efficient neural decoding pipeline that predicts intention by first classifying recordings in short sliding windows. Next, it performs weighted voting over initial predictions up to the current point in time to report a refined final prediction. We demonstrate its utility by classifying spiking neural activity collected from the human posterior parietal cortex for a cue, delay, imaginary motor task. This pipeline provides higher classification accuracy than state-of-the-art time windowed spiking activity based causal methods, and is robust to the choice of hyper-parameters.},
}
@article {pmid34892447,
year = {2021},
author = {Chen, J and Yi, W and Wang, D},
title = {Filter Bank Sinc-ShallowNet with EMD-based Mixed Noise Adding Data Augmentation for Motor Imagery Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5837-5841},
doi = {10.1109/EMBC46164.2021.9629728},
pmid = {34892447},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Imagination ; Neural Networks, Computer ; },
abstract = {Motor imagery-based brain computer interface (MI-BCI) is a representative active BCI paradigm which is widely employed in the rehabilitation field. In MI-BCI, a classification model is built to identify the target limb from MI-based EEG signals, but the performance of models cannot meet the demand for practical use. Lightweight neural networks in deep learning methods are used to build high performance models in MI-BCI. Small sample sizes and the lack of multi-scale information extraction in frequency domain limit the performance improvement of lightweight neural networks. To solve these problems, the Filter Bank Sinc-ShallowNet (FB-Sinc-ShallowNet) algorithm combined with the mixed noise adding method based on empirical mode decomposition (EMD) was proposed. The FB-Sinc-ShallowNet algorithm improves a lightweight neural network Sinc-ShallowNet with a filter bank structure corresponding to four sensory motor rhythms. The mixed noise adding method employs the EMD method to improve the quality of generated data. The proposed method was evaluated on the BCI competition IV IIa dataset and can achieve highest average accuracy of 77.2%, about 6.34% higher than state-of-the-art method Sinc-ShallowNet. This work implies the effectiveness of filter bank structure in lightweight neural networks and provides a novel option for data augmentation and classification of MI-based EEG signals, which can be applied in the rehabilitation field for decoding MI-EEG with few samples.},
}
@article {pmid34892439,
year = {2021},
author = {Su, E and Cai, S and Li, P and Xie, L and Li, H},
title = {Auditory Attention Detection with EEG Channel Attention.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5804-5807},
doi = {10.1109/EMBC46164.2021.9630508},
pmid = {34892439},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Neural Networks, Computer ; Speech ; *Speech Perception ; },
abstract = {Auditory attention detection (AAD) seeks to detect the attended speech from EEG signals in a multi-talker scenario, i.e. cocktail party. As the EEG channels reflect the activities of different brain areas, a task-oriented channel selection technique improves the performance of brain-computer interface applications. In this study, we propose a soft channel attention mechanism, instead of hard channel selection, that derives an EEG channel mask by optimizing the auditory attention detection task. The neural AAD system consists of a neural channel attention mechanism and a convolutional neural network (CNN) classifier. We evaluate the proposed framework on a publicly available database. We achieve 88.3% and 77.2% for 2-second and 0.1-second decision windows with 64-channel EEG; and 86.1% and 83.9% for 2-second decision windows with 32-channel and 16-channel EEG, respectively. The proposed framework outperforms other competitive models by a large margin across all test cases.},
}
@article {pmid34892438,
year = {2021},
author = {Xu, L and Ma, Z and Meng, J and Xu, M and Jung, TP and Ming, D},
title = {Improving Transfer Performance of Deep Learning with Adaptive Batch Normalization for Brain-computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5800-5803},
doi = {10.1109/EMBC46164.2021.9629529},
pmid = {34892438},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Neural Networks, Computer ; },
abstract = {Recently, transfer learning and deep learning have been introduced to solve intra- and inter-subject variability problems in Brain-Computer Interfaces. However, the generalization ability of these BCIs is still to be further verified in a cross-dataset scenario. This study compared the transfer performance of manifold embedded knowledge transfer and pre-trained EEGNet with three preprocessing strategies. This study also introduced AdaBN for target domain adaptation. The results showed that EEGNet with Riemannian alignment and AdaBN could achieve the best transfer accuracy about 65.6% on the target dataset. This study may provide new insights into the design of transfer neural networks for BCIs by separating source and target batch normalization layers in the domain adaptation process.},
}
@article {pmid34892437,
year = {2021},
author = {Chen, XJ and Collins, LM and Mainsah, BO},
title = {Mitigating the Impact of Psychophysical Effects During Adaptive Stimulus Selection in the P300 Speller Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5796-5799},
pmid = {34892437},
issn = {2694-0604},
support = {R21 DC018347/DC/NIDCD NIH HHS/United States ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; Evoked Potentials ; Humans ; },
abstract = {Stimulus-driven brain-computer interfaces (BCIs), such as the P300 speller, rely on using sensory stimuli to elicit specific neural signal components called event-related potentials (ERPs) to control external devices. However, psychophysical factors, such as refractory effects and adjacency distractions, may negatively impact ERP elicitation and BCI performance. Although conventional BCI stimulus presentation paradigms usually design stimulus presentation schedules in a pseudo-random manner, recent studies have shown that controlling the stimulus selection process can enhance ERP elicitation. In prior work, we developed an algorithm to adaptively select BCI stimuli using an objective criterion that maximizes the amount of information about the user's intent that can be elicited with the presented stimuli given current data conditions. Here, we enhance this adaptive BCI stimulus selection algorithm to mitigate adjacency distractions and refractory effects by modeling temporal dependencies of ERP elicitation in the objective function and imposing spatial restrictions in the stimulus search space. Results from simulations using synthetic data and human data from a BCI study show that the enhanced adaptive stimulus selection algorithm can improve spelling speeds relative to conventional BCI stimulus presentation paradigms.Clinical relevance-Increased communication rates with our enhanced adaptive stimulus selection algorithm can potentially facilitate the translation of BCIs as viable communication alternatives for individuals with severe neuromuscular limitations.},
}
@article {pmid34892436,
year = {2021},
author = {Phyo Wai, AA and Ern Tchen, J and Guan, C},
title = {A Study of Visual Search based Calibration Protocol for EEG Attention Detection.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5792-5795},
doi = {10.1109/EMBC46164.2021.9631083},
pmid = {34892436},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Calibration ; Cognition ; *Electroencephalography ; Humans ; Recognition, Psychology ; },
abstract = {Attention, a multi-faceted cognitive process, is essential in our daily lives. We can measure visual attention using an EEG Brain-Computer Interface for detecting different levels of attention in gaming, performance training, and clinical applications. In attention calibration, we use Flanker task to capture EEG data for attentive class. For EEG data belonging to inattentive class calibration, we instruct subject not focusing on a specific position on screen. We then classify attention levels using binary classifier trained with these surrogate ground-truth classes. However, subjects may not be in desirable attention conditions when performing repetitive boring activities over a long experiment duration. We propose attention calibration protocols in this paper that use simultaneous visual search with an audio directional change paradigm and static white noise as 'attentive' and 'inattentive' conditions, respectively. To compare the performance of proposed calibrations against baselines, we collected data from sixteen healthy subjects. For a fair comparison of classification performance; we used six basic EEG band-power features with a standard binary classifier. With the new calibration protocol, we achieved 74.37 ± 6.56% mean subject accuracy, which is about 3.73 ± 2.49% higher than the baseline, but there were no statistically significant differences. According to post-experiment survey results, new calibrations are more effective in inducing desired perceived attention levels. We will improve calibration protocols with reliable attention classifier modeling to enable better attention recognition based on these promising results.},
}
@article {pmid34892433,
year = {2021},
author = {Malekzadeh-Arasteh, O and Pu, H and Danesh, AR and Lim, J and Wang, PT and Liu, CY and Do, AH and Nenadic, Z and Heydari, P},
title = {A Fully-Integrated 1µW/Channel Dual-Mode Neural Data Acquisition System for Implantable Brain-Machine Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5780-5783},
doi = {10.1109/EMBC46164.2021.9630058},
pmid = {34892433},
issn = {2694-0604},
mesh = {Amplifiers, Electronic ; *Brain-Computer Interfaces ; Humans ; Prostheses and Implants ; },
abstract = {This paper presents an ultra-low power mixed-signal neural data acquisition (MSN-DAQ) system that enables a novel low-power hybrid-domain neural decoding architecture for implantable brain-machine interfaces with high channel count. Implemented in 180nm CMOS technology, the 32-channel custom chip operates at 1V supply voltage and achieves excellent performance including 1.07µW/channel, 2.37/5.62 NEF/PEF and 88dB common-mode rejection ratio (CMRR) with significant back-end power-saving advantage compared to prior works. The fabricated prototype was further evaluated with in vivo human tests at bedside, and its performance closely follows that of a commercial recording system.},
}
@article {pmid34892421,
year = {2021},
author = {Adama, S and Bogdan, M},
title = {Yes/No Classification of EEG data from CLIS patients.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5727-5732},
doi = {10.1109/EMBC46164.2021.9629716},
pmid = {34892421},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Cognition ; *Electroencephalography ; Humans ; Support Vector Machine ; },
abstract = {The goal of this research is to evaluate the usability of new features to classify EEG data from several completely locked-in patients (CLIS), and eventually build a more reliable communication system for them. Patients in such state are completely paralyzed, preventing them to be able to talk, but they retain their cognitive abilities.The data were obtained from four CLIS patients and recorded during an auditory paradigm task during which they were asked yes/no questions. Spectral measures such as the relative power of δ, θ, α, β and γ frequency bands, spectral edge frequencies (SEF50 and SEF95), complexity measure obtained from Poincaré plots and connectivity measures such as the imaginary part of coherency and the weighted Symbolic Mutual Information (wSMI) were used as features. The data was classified using Random Forest and Support Vector Machine, two methods successfully used to classify mental states in both healthy subjects and patients. Additionally, two cases were studied. The first case uses data recorded when the patient is answering questions, while in the second case it also includes data recorded when the experimenter is asking the questions.The classification accuracy during training varies between 51.73 to 67.72% in the first case, and from 50.41 to 67.94% for the second case. Overall, wSMI with a time lag of 64 ms gave the best classification accuracy and in general, Random Forest appears to be the best classification method.Clinical relevance This case study investigates the usability of new features based on EEG complexity and connectivity to classify CLIS patients brain signal, what results in a further step toward the demand of more effective EEG-based Brain-Computer Interface communication systems for CLIS patients.},
}
@article {pmid34892414,
year = {2021},
author = {Armengol-Urpi, A and Salazar-Gomez, AF and Sarma, SE},
title = {A Novel Approach to Decode Covert Spatial Attention Using SSVEP and Single-Frequency Phase-Coded Stimuli.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5694-5699},
doi = {10.1109/EMBC46164.2021.9630688},
pmid = {34892414},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; *Visual Cortex ;