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Bibliography on: Brain-Computer Interface

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Robert J. Robbins is a biologist, an educator, a science administrator, a publisher, an information technologist, and an IT leader and manager who specializes in advancing biomedical knowledge and supporting education through the application of information technology. More About:  RJR | OUR TEAM | OUR SERVICES | THIS WEBSITE

RJR: Recommended Bibliography 14 Nov 2022 at 01:59 Created: 

Brain-Computer Interface

Wikipedia: A brain–computer interface (BCI), sometimes called a neural control interface (NCI), mind–machine interface (MMI), direct neural interface (DNI), or brain–machine interface (BMI), is a direct communication pathway between an enhanced or wired brain and an external device. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. Research on BCIs began in the 1970s at the University of California, Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA. The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature. BCI-effected sensory input: Due to the cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels. Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s. BCI-effected motor output: When artificial intelligence is used to decode neural activity, then send that decoded information to some kind of effector device, BCIs have the potential to restore communication to people who have lost the ability to move or speak. To date, the focus has largely been on motor skills such as reaching or grasping. However, in May of 2021 a study showed that an AI/BCI system could be use to translate thoughts about handwriting into the output of legible characters at a usable rate (90 characters per minute with 94% accuracy).

Created with PubMed® Query: bci OR (brain-computer interface) OR (brain-machine interface) OR (mind-machine interface) OR (neural-control interface) NOT 26799652[PMID] NOT pmcbook NOT ispreviousversion

Citations The Papers (from PubMed®)

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RevDate: 2022-11-09

McNamara CG, Rothwell M, A Sharott (2022)

Stable, interactive modulation of neuronal oscillations produced through brain-machine equilibrium.

Cell reports, 41(6):111616.

Closed-loop interaction has the potential to regulate ongoing brain activity by continuously binding an external stimulation to specific dynamics of a neural circuit. Achieving interactive modulation requires a stable brain-machine feedback loop. Here, we demonstrate that it is possible to maintain oscillatory brain activity in a desired state by delivering stimulation accurately aligned with the timing of each cycle. We develop a fast algorithm that responds on a cycle-by-cycle basis to stimulate basal ganglia nuclei at predetermined phases of successive cortical beta cycles in parkinsonian rats. Using this approach, an equilibrium emerges between the modified brain signal and feedback-dependent stimulation pattern, leading to sustained amplification or suppression of the oscillation depending on the phase targeted. Beta amplification slows movement speed by biasing the animal's mode of locomotion. Together, these findings show that highly responsive, phase-dependent stimulation can achieve a stable brain-machine interaction that leads to robust modulation of ongoing behavior.

RevDate: 2022-11-09

Zhang M, Wu J, Song J, et al (2022)

Decoding Coordinated Directions of Bimanual Movements from EEG Signals.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Bimanual coordination is common in human daily life, whereas current research focused mainly on decoding unimanual movement from electroencephalogram (EEG) signals. Here we developed a brain-computer interface (BCI) paradigm of task-oriented bimanual movements to decode coordinated directions from movement-related cortical potentials (MRCPs) of EEG. Eight healthy subjects participated in the target-reaching task, including (1) performing leftward, midward, and rightward bimanual movements, and (2) performing leftward and rightward unimanual movements. A combined deep learning model of convolution neural network and bidirectional long short-term memory network was proposed to classify movement directions from EEG. Results showed that the average peak classification accuracy for three coordinated directions of bimanual movements reached 73.39 ± 6.35%. The binary classification accuracies achieved 80.24 ± 6.25, 82.62 ± 7.82, and 86.28 ± 5.50% for leftward versus midward, rightward versus midward and leftward versus rightward, respectively. We also compared the binary classification (leftward versus rightward) of bimanual, left-hand, and right-hand movements, and accuracies achieved 86.28 ± 5.50%, 75.67 ± 7.18%, and 77.79 ± 5.65%, respectively. The results indicated the feasibility of decoding human coordinated directions of task-oriented bimanual movements from EEG.

RevDate: 2022-11-09

Sattler S, D Pietralla (2022)

Public attitudes towards neurotechnology: Findings from two experiments concerning Brain Stimulation Devices (BSDs) and Brain-Computer Interfaces (BCIs).

PloS one, 17(11):e0275454 pii:PONE-D-22-11587.

This study contributes to the emerging literature on public perceptions of neurotechnological devices (NTDs) in their medical and non-medical applications, depending on their invasiveness, framing effects, and interindividual differences related to personal needs and values. We conducted two web-based between-subject experiments (2×2×2) using a representative, nation-wide sample of the adult population in Germany. Using vignettes describing how two NTDs, brain stimulation devices (BSDs; NExperiment 1 = 1,090) and brain-computer interfaces (BCIs; NExperiment 2 = 1,089), function, we randomly varied the purpose (treatment vs. enhancement) and invasiveness (noninvasive vs. invasive) of the NTD, and assessed framing effects (variable order of assessing moral acceptability first vs. willingness to use first). We found a moderate moral acceptance and willingness to use BSDs and BCIs. Respondents preferred treatment over enhancement purposes and noninvasive over invasive devices. We also found a framing effect and explored the role of personal characteristics as indicators of personal needs and values (e.g., stress, religiosity, and gender). Our results suggest that the future demand for BSDs or BCIs may depend on the purpose, invasiveness, and personal needs and values. These insights can inform technology developers about the public's needs and concerns, and enrich legal and ethical debates.

RevDate: 2022-11-09

Zwerus EL, van Deurzen DFP, van den Bekerom MPJ, et al (2022)

Distal Biceps Tendon Ruptures: Diagnostic Strategy Through Physical Examination.

The American journal of sports medicine [Epub ahead of print].

BACKGROUND: Distinguishing a complete from a partial distal biceps tendon rupture is essential, as a complete rupture may require repair on short notice to restore function, whereas partial ruptures can be treated nonsurgically in most cases. Reliability of physical examination is crucial to determine the right workup and treatment in patients with a distal biceps tendon rupture.

PURPOSES: The primary aim of this study was to find a (combination of) test(s) that serves best to diagnose a complete rupture with certainty in the acute phase (≤1 month) without missing any complete ruptures. The secondary aims were to determine the best (combination of) test(s) to identify a chronic (>1 month) rupture of the distal biceps tendon and indicate additional imaging in case partial ruptures or tendinitis are suspected.

STUDY DESIGN: Cohort study (Diagnosis); Level of evidence, 2.

METHODS: A total of 86 patients with anterior elbow complaints or suspected distal biceps injury underwent standardized physical examination, including the Hook test, passive forearm pronation test, biceps crease interval (BCI), and biceps crease ratio. Diagnosis was confirmed intraoperatively (68 cases), by magnetic resonance imaging (13 cases), or by ultrasound (5 cases).

RESULTS: A combination of the Hook test and BCI (ie, both tests are positive) was most accurate for both acute and chronic ruptures but with a different purpose. For acute complete ruptures, sensitivity was 94% and specificity was 100%. In chronic cases, specificity was also 100%. Weakness on active supination and palpation of the tendon footprint provided excellent sensitivity of 100% for chronic complete ruptures and partial ruptures, respectively.

CONCLUSION: The combination of a positive Hook test and BCI serves best to accurately diagnose acute complete ruptures of the distal biceps tendon. Weakness on active supination and pain on palpation of the tendon footprint provide excellent sensitivity for chronic complete ruptures and partial ruptures. Using these tests in all suspected distal biceps ruptures allows a physician to refrain from imaging for a diagnostic purpose in certain cases, to limit treatment delay and thereby provide better treatment outcome, and to avoid hospital and social costs.

RevDate: 2022-11-09

Peterson V, Merk T, Bush A, et al (2022)

Movement decoding using spatio-spectral features of cortical and subcortical local field potentials.

Experimental neurology, 359:114261 pii:S0014-4886(22)00286-2 [Epub ahead of print].

The first commercially sensing enabled deep brain stimulation (DBS) devices for the treatment of movement disorders have recently become available. In the future, such devices could leverage machine learning based brain signal decoding strategies to individualize and adapt therapy in real-time. As multi-channel recordings become available, spatial information may provide an additional advantage for informing machine learning models. To investigate this concept, we compared decoding performances from single channels vs. spatial filtering techniques using intracerebral multitarget electrophysiology in Parkinson's disease patients undergoing DBS implantation. We investigated the feasibility of spatial filtering in invasive neurophysiology and the putative utility of combined cortical ECoG and subthalamic local field potential signals for decoding grip-force, a well-defined and continuous motor readout. We found that adding spatial information to the model can improve decoding (6% gain in decoding), but the spatial patterns and additional benefit was highly individual. Beyond decoding performance results, spatial filters and patterns can be used to obtain meaningful neurophysiological information about the brain networks involved in target behavior. Our results highlight the importance of individualized approaches for brain signal decoding, for which multielectrode recordings and spatial filtering can improve precision medicine approaches for clinical brain computer interfaces.

RevDate: 2022-11-09

Khademi Z, Ebrahimi F, HM Kordy (2022)

A review of critical challenges in MI-BCI: From conventional to deep learning methods.

Journal of neuroscience methods, 383:109736 pii:S0165-0270(22)00262-X [Epub ahead of print].

Brain-computer interfaces (BCIs) have achieved significant success in controlling external devices through the Electroencephalogram (EEG) signal processing. BCI-based Motor Imagery (MI) system bridges brain and external devices as communication tools to control, for example, wheelchair for people with disabilities, robotic control, and exoskeleton control. This success largely depends on the machine learning (ML) approaches like deep learning (DL) models. DL algorithms provide effective and powerful models to analyze compact and complex EEG data optimally for MI-BCI applications. DL models with CNN network have revolutionized computer vision through end-to-end learning from raw data. Meanwhile, RNN networks have been able to decode EEG signals by processing sequences of time series data. However, many challenges in the MI-BCI field have affected the performance of DL models. A major challenge is the individual differences in the EEG signal of different subjects. Therefore, the model must be retrained from the scratch for each new subject, which leads to computational costs. Analyzing the EEG signals is challenging due to its low signal to noise ratio and non-stationary nature. Additionally, limited size of existence datasets can lead to overfitting which can be prevented by using transfer learning (TF) approaches. The main contributions of this study are discovering major challenges in the MI-BCI field by reviewing the state of art machine learning models and then suggesting solutions to address these challenges by focusing on feature selection, feature extraction and classification methods.

RevDate: 2022-11-08

Fu K, Du C, Wang S, et al (2022)

Multi-View Multi-Label Fine-Grained Emotion Decoding From Human Brain Activity.

IEEE transactions on neural networks and learning systems, PP: [Epub ahead of print].

Decoding emotional states from human brain activity play an important role in the brain-computer interfaces. Existing emotion decoding methods still have two main limitations: one is only decoding a single emotion category from a brain activity pattern and the decoded emotion categories are coarse-grained, which is inconsistent with the complex emotional expression of humans; the other is ignoring the discrepancy of emotion expression between the left and right hemispheres of the human brain. In this article, we propose a novel multi-view multi-label hybrid model for fine-grained emotion decoding (up to 80 emotion categories) which can learn the expressive neural representations and predict multiple emotional states simultaneously. Specifically, the generative component of our hybrid model is parameterized by a multi-view variational autoencoder, in which we regard the brain activity of left and right hemispheres and their difference as three distinct views and use the product of expert mechanism in its inference network. The discriminative component of our hybrid model is implemented by a multi-label classification network with an asymmetric focal loss. For more accurate emotion decoding, we first adopt a label-aware module for emotion-specific neural representation learning and then model the dependency of emotional states by a masked self-attention mechanism. Extensive experiments on two visually evoked emotional datasets show the superiority of our method.

RevDate: 2022-11-08

Sake SM, Kosch C, Blockus S, et al (2022)

Respiratory Syncytial Virus Two-Step Infection Screen Reveals Inhibitors of Early and Late Life Cycle Stages.

Antimicrobial agents and chemotherapy [Epub ahead of print].

Human respiratory syncytial virus (hRSV) infection is a leading cause of severe respiratory tract infections. Effective, directly acting antivirals against hRSV are not available. We aimed to discover new and chemically diverse candidates to enrich the hRSV drug development pipeline. We used a two-step screen that interrogates compound efficacy after primary infection and a consecutive virus passaging. We resynthesized selected hit molecules and profiled their activities with hRSV lentiviral pseudotype cell entry, replicon, and time-of-addition assays. The breadth of antiviral activity was tested against recent RSV clinical strains and human coronavirus (hCoV-229E), and in pseudotype-based entry assays with non-RSV viruses. Screening 6,048 molecules, we identified 23 primary candidates, of which 13 preferentially scored in the first and 10 in the second rounds of infection, respectively. Two of these molecules inhibited hRSV cell entry and selected for F protein resistance within the fusion peptide. One molecule inhibited transcription/replication in hRSV replicon assays, did not select for phenotypic hRSV resistance and was active against non-hRSV viruses, including hCoV-229E. One compound, identified in the second round of infection, did not measurably inhibit hRSV cell entry or replication/transcription. It selected for two coding mutations in the G protein and was highly active in differentiated BCi-NS1.1 lung cells. In conclusion, we identified four new hRSV inhibitor candidates with different modes of action. Our findings build an interesting platform for medicinal chemistry-guided derivatization approaches followed by deeper phenotypical characterization in vitro and in vivo with the aim of developing highly potent hRSV drugs.

RevDate: 2022-11-07

Bijari K, Zoubi Y, GA Ascoli (2022)

Assisted neuroscience knowledge extraction via machine learning applied to neural reconstruction metadata on NeuroMorpho.Org.

Brain informatics, 9(1):26.

The amount of unstructured text produced daily in scholarly journals is enormous. Systematically identifying, sorting, and structuring information from such a volume of data is increasingly challenging for researchers even in delimited domains. Named entity recognition is a fundamental natural language processing tool that can be trained to annotate, structure, and extract information from scientific articles. Here, we harness state-of-the-art machine learning techniques and develop a smart neuroscience metadata suggestion system accessible by both humans through a user-friendly graphical interface and machines via Application Programming Interface. We demonstrate a practical application to the public repository of neural reconstructions, NeuroMorpho.Org, thus expanding the existing web-based metadata management system currently in use. Quantitative analysis indicates that the suggestion system reduces personnel labor by at least 50%. Moreover, our results show that larger training datasets with the same software architecture are unlikely to further improve performance without ad-hoc heuristics due to intrinsic ambiguities in neuroscience nomenclature. All components of this project are released open source for community enhancement and extensions to additional applications.

RevDate: 2022-11-08
CmpDate: 2022-11-08

Smrdel A (2022)

Use of common spatial patterns for early detection of Parkinson's disease.

Scientific reports, 12(1):18793.

One of the most common diseases that affects human brain is Parkinson's disease. Detection of Parkinson's disease (PD) poses a serious challenge. Robust methods for feature extraction allowing separation between the electroencephalograms (EEG) of healthy subjects and PD patients are required. We used the EEG records of healthy subjects and PD patients which were subject to auditory tasks. We used the common spatial patterns (CSP) and Laplacian mask as methods to allow robust selection and extraction of features. We used the derived CSP whitening matrix to determine those channels that are the most promising in the terms of differentiating between EEGs of healthy controls and of PD patients. Using the selection of features calculated using the CSP we managed to obtain the classification accuracy of 85% when classifying EEG records belonging to groups of controls or PD patients. Using the features calculated using the Laplacian operator we obtained the classification accuracy of 90%. Diagnosing the PD in early stages using EEG is possible. The CSP proved to be a promising technique to detect informative channels and to separate between the groups. Use of the combination of features calculated using the Laplacian offers good separability between the two groups.

RevDate: 2022-11-07

Pu X, Yi P, Chen K, et al (2022)

EEGDnet: Fusing non-local and local self-similarity for EEG signal denoising with transformer.

Computers in biology and medicine, 151(Pt A):106248 pii:S0010-4825(22)00956-8 [Epub ahead of print].

Electroencephalogram (EEG) has shown a useful approach to produce a brain-computer interface (BCI). One-dimensional (1-D) EEG signal is yet easily disturbed by certain artifacts (a.k.a. noise) due to the high temporal resolution. Thus, it is crucial to remove the noise in received EEG signal. Recently, deep learning-based EEG signal denoising approaches have achieved impressive performance compared with traditional ones. It is well known that the characteristics of self-similarity (including non-local and local ones) of data (e.g., natural images and time-domain signals) are widely leveraged for denoising. However, existing deep learning-based EEG signal denoising methods ignore either the non-local self-similarity (e.g., 1-D convolutional neural network) or local one (e.g., fully connected network and recurrent neural network). To address this issue, we propose a novel 1-D EEG signal denoising network with 2-D transformer, namely EEGDnet. Specifically, we comprehensively take into account the non-local and local self-similarity of EEG signal through the transformer module. By fusing non-local self-similarity in self-attention blocks and local self-similarity in feed forward blocks, the negative impact caused by noises and outliers can be reduced significantly. Extensive experiments show that, compared with other state-of-the-art models, EEGDnet achieves much better performance in terms of both quantitative and qualitative metrics. Specifically, EEGDnet can achieve 18% and 11% improvements in correlation coefficients when removing ocular artifacts and muscle artifacts, respectively.

RevDate: 2022-11-07

Zhang D, Liu S, Zhang J, et al (2022)

Brain-Controlled 2D Navigation Robot Based on a Spatial Gradient Controller and Predictive Environmental Coordinator.

IEEE journal of biomedical and health informatics, PP: [Epub ahead of print].

OBJECTIVE: Brain-computer interfaces (BCIs) have been used in two-dimensional (2D) navigation robotic devices, such as brain-controlled wheelchairs and brain-controlled vehicles. However, contemporary BCI systems are driven by binary selective control. On the one hand, only directional information can be transferred from humans to machines, such as "turn left" or "turn right", which means that the quantified value, such as the radius of gyration, cannot be controlled. In this study, we proposed a spatial gradient BCI controller and corresponding environment coordinator, by which the quantified value of brain commands can be transferred in the form of a 2D vector, improving the flexibility, stability and efficiency of BCIs.

METHODS: A horizontal array of steady-state visual stimulation was arranged to excite subject (EEG) signals. Covariance arrays between subjects' electroencephalogram (EEG) and stimulation features were mapped into quantified 2-dimensional vectors. The generated vectors were then inputted into the predictive controller and fused with virtual forces generated by the robot's predictive environment coordinator in the form of vector calculation. The resultant vector was then interpreted into the driving force for the robot, and real-time speed feedback was generated.

RESULTS: The proposed SGC controller generated a faster (27.4 s vs. 34.9 s) response for the single-obstacle avoidance task than the selective control approach. In practical multiobstacle tasks, the proposed robot executed 39% faster in the target-reaching tasks than the selective controller and had better robustness in multiobstacle avoidance tasks (average failures significantly dropped from 27% to 4%).

SIGNIFICANCE: This research proposes a new form of brain-machine shared control strategy that quantifies brain commands in the form of a 2-D control vector stream rather than selective constant values. Combined with a predictive environment coordinator, the brain-controlled strategy of the robot is optimized and provided with higher flexibility. The proposed controller can be used in brain-controlled 2D navigation devices, such as brain-controlled wheelchairs and vehicles.

RevDate: 2022-11-07

Lee HS, Schreiner L, Jo SH, et al (2022)

Individual finger movement decoding using a novel ultra-high-density electroencephalography-based brain-computer interface system.

Frontiers in neuroscience, 16:1009878.

Brain-Computer Interface (BCI) technology enables users to operate external devices without physical movement. Electroencephalography (EEG) based BCI systems are being actively studied due to their high temporal resolution, convenient usage, and portability. However, fewer studies have been conducted to investigate the impact of high spatial resolution of EEG on decoding precise body motions, such as finger movements, which are essential in activities of daily living. Low spatial sensor resolution, as found in common EEG systems, can be improved by omitting the conventional standard of EEG electrode distribution (the international 10-20 system) and ordinary mounting structures (e.g., flexible caps). In this study, we used newly proposed flexible electrode grids attached directly to the scalp, which provided ultra-high-density EEG (uHD EEG). We explored the performance of the novel system by decoding individual finger movements using a total of 256 channels distributed over the contralateral sensorimotor cortex. Dense distribution and small-sized electrodes result in an inter-electrode distance of 8.6 mm (uHD EEG), while that of conventional EEG is 60 to 65 mm on average. Five healthy subjects participated in the experiment, performed single finger extensions according to a visual cue, and received avatar feedback. This study exploits mu (8-12 Hz) and beta (13-25 Hz) band power features for classification and topography plots. 3D ERD/S activation plots for each frequency band were generated using the MNI-152 template head. A linear support vector machine (SVM) was used for pairwise finger classification. The topography plots showed regular and focal post-cue activation, especially in subjects with optimal signal quality. The average classification accuracy over subjects was 64.8 (6.3)%, with the middle versus ring finger resulting in the highest average accuracy of 70.6 (9.4)%. Further studies are required using the uHD EEG system with real-time feedback and motor imagery tasks to enhance classification performance and establish the basis for BCI finger movement control of external devices.

RevDate: 2022-11-07

Tang C, Gao T, Li Y, et al (2022)

EEG channel selection based on sequential backward floating search for motor imagery classification.

Frontiers in neuroscience, 16:1045851.

Brain-computer interfaces (BCIs) based on motor imagery (MI) utilizing multi-channel electroencephalogram (EEG) data are commonly used to improve motor function of people with motor disabilities. EEG channel selection can enhance MI classification accuracy by selecting informative channels, accordingly reducing redundant information. The sequential backward floating search (SBFS) approach has been considered as one of the best feature selection methods. In this paper, SBFS is first implemented to select the optimal EEG channels in MI-BCI. Further, to reduce the time complexity of SBFS, the modified SBFS is proposed and applied to left and right hand MI tasks. In the modified SBFS, based on the map of EEG channels at the scalp, the symmetrical channels are selected as channel pairs and acceleration is thus realized by removing or adding multiple channels in each iteration. Extensive experiments were conducted on four public BCI datasets. Experimental results show that the SBFS achieves significantly higher classification accuracy (p < 0.001) than using all channels and conventional MI channels (i.e., C3, C4, and Cz). Moreover, the proposed method outperforms the state-of-the-art selection methods.

RevDate: 2022-11-07

He C, Du Y, X Zhao (2022)

A separable convolutional neural network-based fast recognition method for AR-P300.

Frontiers in human neuroscience, 16:986928.

Augmented reality-based brain-computer interface (AR-BCI) has a low signal-to-noise ratio (SNR) and high real-time requirements. Classical machine learning algorithms that improve the recognition accuracy through multiple averaging significantly affect the information transfer rate (ITR) of the AR-SSVEP system. In this study, a fast recognition method based on a separable convolutional neural network (SepCNN) was developed for an AR-based P300 component (AR-P300). SepCNN achieved single extraction of AR-P300 features and improved the recognition speed. A nine-target AR-P300 single-stimulus paradigm was designed to be administered with AR holographic glasses to verify the effectiveness of SepCNN. Compared with four classical algorithms, SepCNN significantly improved the average target recognition accuracy (81.1%) and information transmission rate (57.90 bits/min) of AR-P300 single extraction. SepCNN with single extraction also attained better results than classical algorithms with multiple averaging.

RevDate: 2022-11-07

Floreani ED, Rowley D, Kelly D, et al (2022)

On the feasibility of simple brain-computer interface systems for enabling children with severe physical disabilities to explore independent movement.

Frontiers in human neuroscience, 16:1007199.

Introduction: Children with severe physical disabilities are denied their fundamental right to move, restricting their development, independence, and participation in life. Brain-computer interfaces (BCIs) could enable children with complex physical needs to access power mobility (PM) devices, which could help them move safely and independently. BCIs have been studied for PM control for adults but remain unexamined in children. In this study, we explored the feasibility of BCI-enabled PM control for children with severe physical disabilities, assessing BCI performance, standard PM skills and tolerability of BCI.

Materials and methods: Patient-oriented pilot trial. Eight children with quadriplegic cerebral palsy attended two sessions where they used a simple, commercial-grade BCI system to activate a PM trainer device. Performance was assessed through controlled activation trials (holding the PM device still or activating it upon verbal and visual cueing), and basic PM skills (driving time, number of activations, stopping) were assessed through distance trials. Setup and calibration times, headset tolerability, workload, and patient/caregiver experience were also evaluated.

Results: All participants completed the study with favorable tolerability and no serious adverse events or technological challenges. Average control accuracy was 78.3 ± 12.1%, participants were more reliably able to activate (95.7 ± 11.3%) the device than hold still (62.1 ± 23.7%). Positive trends were observed between performance and prior BCI experience and age. Participants were able to drive the PM device continuously an average of 1.5 meters for 3.0 s. They were able to stop at a target 53.1 ± 23.3% of the time, with significant variability. Participants tolerated the headset well, experienced mild-to-moderate workload and setup/calibration times were found to be practical. Participants were proud of their performance and both participants and families were eager to participate in future power mobility sessions.

Discussion: BCI-enabled PM access appears feasible in disabled children based on evaluations of performance, tolerability, workload, and setup/calibration. Performance was comparable to existing pediatric BCI literature and surpasses established cut-off thresholds (70%) of "effective" BCI use. Participants exhibited PM skills that would categorize them as "emerging operational learners." Continued exploration of BCI-enabled PM for children with severe physical disabilities is justified.

RevDate: 2022-11-07

Li L (2022)

Preface to special topic on brain-machine interface.

National science review, 9(10):nwac211 pii:nwac211.

RevDate: 2022-11-07

Song X, Zeng Y, Tong L, et al (2022)

Corrigendum to "A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual Targets".

Computational intelligence and neuroscience, 2022:9819804.

[This corrects the article DOI: 10.1155/2022/4752450.].

RevDate: 2022-11-05

Gao Z, Pang Z, Chen Y, et al (2022)

Restoring After Central Nervous System Injuries: Neural Mechanisms and Translational Applications of Motor Recovery.

Neuroscience bulletin [Epub ahead of print].

Central nervous system (CNS) injuries, including stroke, traumatic brain injury, and spinal cord injury, are leading causes of long-term disability. It is estimated that more than half of the survivors of severe unilateral injury are unable to use the denervated limb. Previous studies have focused on neuroprotective interventions in the affected hemisphere to limit brain lesions and neurorepair measures to promote recovery. However, the ability to increase plasticity in the injured brain is restricted and difficult to improve. Therefore, over several decades, researchers have been prompted to enhance the compensation by the unaffected hemisphere. Animal experiments have revealed that regrowth of ipsilateral descending fibers from the unaffected hemisphere to denervated motor neurons plays a significant role in the restoration of motor function. In addition, several clinical treatments have been designed to restore ipsilateral motor control, including brain stimulation, nerve transfer surgery, and brain-computer interface systems. Here, we comprehensively review the neural mechanisms as well as translational applications of ipsilateral motor control upon rehabilitation after CNS injuries.

RevDate: 2022-11-04

Zhan Q, Wang L, Ren L, et al (2022)

A novel heterogeneous transfer learning method based on data stitching for the sequential coding brain computer interface.

Computers in biology and medicine, 151(Pt A):106220 pii:S0010-4825(22)00928-3 [Epub ahead of print].

OBJECTIVE: For the brain computer interface (BCI), it is necessary to collect enough electroencephalography (EEG) signals to train the classification model. When the operation dimension of BCI is large, it will bring great burden to data acquisition. Fortunately, this problem can be solved by our proposed transfer learning method.

METHOD: For the sequential coding experimental paradigm, the multi-band data stitching with label alignment and tangent space mapping (MDSLATSM) algorithm is proposed as a novel heterogeneous transfer learning method. After filtering by multi-band filtering, the artificial signals can be obtained by data stitching from the source domain, which build a bridge between the source domain and target domain. To make the distribution of two domains closer, their covariance matrices are aligned by label alignment. After mapping to the tangent space, the features are extracted from the Riemannian manifold. Finally, the classification results are obtained with feature selection and classification.

RESULTS: Our data set includes the EEG signals from 16 subjects. For the heterogeneous transfer learning of cross-label, the average classification accuracy is 78.28%. MDSLATSM is also tested for cross-subject, and the average classification accuracy is 64.01%, which is better than existing methods.

SIGNIFICANCE: Combining multi-band filtering, data stitching, label alignment and tangent space mapping, a novel heterogeneous transfer learning method can be achieved with superior performance, which promotes the practical application of the BCI systems.

RevDate: 2022-11-04

Zhang S, Wu L, Yu S, et al (2022)

An Explainable and Generalizable Recurrent Neural Network Approach for Differentiating Human Brain States on EEG Dataset.

IEEE transactions on neural networks and learning systems, PP: [Epub ahead of print].

Electroencephalogram (EEG) is one of the most widely used brain computer interface (BCI) approaches. Despite the success of existing EEG approaches in brain state recognition studies, it is still challenging to differentiate brain states via explainable and generalizable deep learning approaches. In other words, how to explore meaningful and distinguishing features and how to overcome the huge variability and overfitting problem still need to be further studied. To alleviate these challenges, in this work, a multiple random fragment search-based multilayer recurrent neural network (MRFS-MRNN) is proposed to improve the differentiating performance and explore meaningful patterns. Specifically, an explainable MRNN module is proposed to capture the temporal dependences preserved in EEG time series. Besides, a MRFS module is designed to cut multiple random fragments from the entire EEG signal time course to improve the effectiveness of brain state differentiating ability. MRFS-MRNN is concatenatedto effectively overcome the huge variabilities and overfitting problems. Experiment results demonstrate that the proposed MRFS-MRNN model not only has excellent differentiating performance, but also has good explanation and generalization ability. The classification accuracies reach as high as 95.18% for binary classification and 89.19% for four-category classification on the individual level. Similarly, 95.53% and 85.84% classification accuracies are obtained for the binary and four-category classification on the group level. What's more, 94.28% and 85.43% classification accuracies of binary and four-category classifications are achieved for predicting brand new subjects. The experiment results showed that the proposed method outperformed other state-of-the-art (SOTA) models on the same underlying data and improved the explanation and generalization ability.

RevDate: 2022-11-04

Wang R, Liu Y, Shi J, et al (2022)

Sound Target Detection under Noisy Environment Using Brain-Computer Interface.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

As an important means of environmental reconnaissance and regional security protection, sound target detection (STD) has been widely studied in the field of machine learning for a long time. Considering the shortcomings of the robustness and generalization performance of existing methods based on machine learning, we proposed a target detection method by an auditory brain-computer interface (BCI). We designed the experimental paradigm according to the actual application scenarios of STD, recorded the changes in Electroencephalogram (EEG) signals during the process of detecting target sound, and further extracted the features used to decode EEG signals through the analysis of neural representations, including Event-Related Potential (ERP) and Event-Related Spectral Perturbation (ERSP). Experimental results showed that the proposed method achieved good detection performance under noisy environment. As the first study of BCI applied to STD, this study shows the feasibility of this scheme in BCI and can serve as the foundation for future related applications.

RevDate: 2022-11-04

Mencel J, Marusiak J, Jaskólska A, et al (2022)

Motor imagery training of goal-directed reaching in relation to imagery of reaching and grasping in healthy people.

Scientific reports, 12(1):18610.

The study aimed to determine whether four weeks of motor imagery training (MIT) of goal-directed reaching (reaching to grasp task) would affect the cortical activity during motor imagery of reaching (MIR) and grasping (MIG) in the same way. We examined cortical activity regarding event-related potentials (ERPs) in healthy young participants. Our study also evaluated the subjective vividness of the imagery. Furthermore, we aimed to determine the relationship between the subjective assessment of motor imagery (MI) ability to reach and grasp and the cortical activity during those tasks before and after training to understand the underlying neuroplasticity mechanisms. Twenty-seven volunteers participated in MIT of goal-directed reaching and two measurement sessions before and after MIT. During the sessions 128-channel electroencephalography (EEG) was recorded during MIR and MIG. Also, participants assessed the vividness of the MI tasks using a visual analog scale (VAS). The vividness of imagination improved significantly (P < .05) after MIT. A repeated measures ANOVA showed that the task (MIR/MIG) and the location of electrodes had a significant effect on the ERP's amplitude (P < .05). The interaction between the task, location, and session (before/after MIT) also had a significant effect on the ERP's amplitude (P < .05). Finally, the location of electrodes and the interaction between location and session had a significant effect on the ERP's latency (P < .05). We found that MIT influenced the EEG signal associated with reaching differently than grasping. The effect was more pronounced for MIR than for MIG. Correlation analysis showed that changes in the assessed parameters due to MIT reduced the relationship between the subjective evaluation of imagining and the EEG signal. This finding means that the subjective evaluation of imagining cannot be a simple, functional insight into the bioelectrical activity of the cerebral cortex expressed by the ERPs in mental training. The changes we noted in ERPs after MIT may benefit the use of non-invasive EEG in the brain-computer interface (BCI) context.Trial registration: NCT04048083.

RevDate: 2022-11-03
CmpDate: 2022-11-03

Ho S, Liu P, Palombo DJ, et al (2022)

The role of spatial ability in mixed reality learning with the HoloLens.

Anatomical sciences education, 15(6):1074-1085.

The use of mixed reality in science education has been increasing and as such it has become more important to understand how information is learned in these virtual environments. Spatial ability is important in many learning contexts, but especially in neuroanatomy education where learning the locations and spatial relationships between brain regions is paramount. It is currently unclear what role spatial ability plays in mixed reality learning environments, and whether it is different compared to traditional physical environments. To test this, a learning experiment was conducted where students learned neuroanatomy using both mixed reality and a physical plastic model of a brain (N = 27). Spatial ability was assessed and analyzed to determine its effect on performance across the two learning modalities. The results showed that spatial ability facilitated learning in mixed reality (β = 0.21, P = 0.003), but not when using a plastic model (β = 0.08, P = 0.318). A non-significant difference was observed between the modalities in terms of knowledge test performance (d = 0.39, P = 0.052); however, mixed reality was more engaging (d = 0.59, P = 0.005) and learners were more confident in the information they learned compared to using a physical model (d = 0.56, P = 0.007). Overall, these findings suggest that spatial ability is more relevant in virtual learning environments, where the ability to manipulate and interact with an object is diminished or abstracted through a virtual user interface.

RevDate: 2022-11-01

Bian Y, Zhao L, Li J, et al (2022)

Improvements in classification of left and right foot motor intention using modulated steady-state somatosensory evoked potential induced by electrical stimulation and motor imagery.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

In recent years, motor imagery-based brain-computer interface (MI-BCI) has been applied to motor rehabilitation in patients with motor dysfunction. However, traditional MI-BCI is rarely used for foot motor intention recognition because the motor cortex regions of both feet are anatomically close to each other, and traditional event-related desynchronization (ERD) patterns for MI-BCI have insufficient spatial discrimination. This study introduced steady-state somatosensory evoked potentials (SSSEPs) by synchronous bilateral feet electrical stimulation at different frequencies, which were used as carrier signals modulated by unilateral foot motor intention. Fifteen subjects participated in MI and MI-SSSEP tasks. A Riemannian geometry classifier with a task-related component analysis (TRCA) spatial filter was proposed to demodulate the variation in SSSEP features and discriminate the left and right foot motor intentions. The feature outcomes indicated that the amplitude and phase synchronization of the SSSEPs could be well modulated by unilateral foot MI tasks under the MI-SSSEP paradigm. The classification results revealed that the modulated SSSEP features played an important role in the recognition of left-right foot discrimination. Moreover, the proposed TRCA-based method outperformed the other three methods and improved the foot average classification accuracy to 81.07±13.29%, with the highest accuracy attained at 97.00%. Compared with the traditional MI paradigm, the foot motor intention recognition accuracy of the MI-SSSEP paradigm was significantly improved, from nearly 60% to more than 80%. This work provides a practical method for left-right foot motor intention recognition and expands the application of MI-BCI in the field of lower-extremity motor function rehabilitation.

RevDate: 2022-11-01

Ogino M, Hamada N, Y Mitsukura (2022)

Simultaneous multiple-stimulus auditory brain-computer interface with semi-supervised learning and prior probability distribution tuning.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Auditory brain-computer interfaces (BCIs) enable users to select commands based on the brain activity elicited by auditory stimuli. However,existing auditory BCI paradigms cannot increase the number of available commands without decreasing the selection speed, because each stimulus needs to be presented independently and sequentially under the standard oddball paradigm. To solve this problem, we propose a double-stimulus paradigm that simultaneously presents multiple auditory stimuli.

APPROACH: For addition to an existing auditory BCI paradigm, the best discriminable sound was chosen following a subjective assessment. The new sound was located on the right-hand side and presented simultaneously with an existing sound from the left-hand side. A total of six sounds were used for implementing the auditory BCI with a 6 × 6 letter matrix. We employ semi-supervised learning (SSL) and prior probability distribution tuning to improve the accuracy of the paradigm. The SSL method involved updating of the classifier weights, and their prior probability distributions were adjusted using the following three types of distributions: uniform, empirical, and extended empirical (e-empirical). The performance was evaluated based on the BCI accuracy and information transfer rate (ITR).

MAIN RESULTS: The double-stimulus paradigm resulted in a BCI accuracy of 67.89 ±11.46% and an ITR of 2.67 ± 1.09 bits/min, in the absence of SSL and with uniform distribution. The proposed combination of SSL with e-empirical distribution improved the BCI accuracy and ITR to 74.59 ± 12.12% and 3.37 ± 1.27 bits/min, respectively. The ERP analysis revealed that contralateral and right-hemispheric dominances contributed to the BCI performance improvement.

SIGNIFICANCE: Our study demonstrated that a BCI based on multiple simultaneous auditory stimuli, incorporating SSL and e-empirical prior distribution, can increase the number of commands without sacrificing typing speed beyond the acceptable level of accuracy.

RevDate: 2022-11-01

Jia H, Sun Z, Duan F, et al (2022)

Improving pre-movement pattern detection with filter bank selection.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Pre-movement decoding plays an important role in detecting the onsets of actions using low-frequency electroencephalography (EEG) signals before the movement of an upper limb. In this work, a binary classification method is proposed between two different states.

APPROACH: The proposed method, referred to as filter bank standard task-related component analysis (FBTRCA), is to incorporate filter bank selection into the standard task-related component analysis (STRCA) method. In FBTRCA, the EEG signals are first divided into multiple sub-bands which start at specific fixed frequencies and end frequencies that follow in an arithmetic sequence. The STRCA method is then applied to the EEG signals in these bands to extract canonical correlation patterns. The minimum redundancy maximum relevance feature selection method is used to select essential features from these correlation patterns in all sub-bands. Finally, the selected features are classified using the binary support vector machine classifier. A convolutional neural network (CNN) was also used to select canonical correlation patterns.

RESULTS: Three methods were evaluated using EEG signals in the readiness potential section. The readiness potential section is a two-second time window before the movement onset. In the binary classification between a movement state and the resting state, the FBTRCA achieved an average accuracy of 0.8666±0.1038 while the accuracies of STRCA and CNN were 0.8287±0.1101 and 0.8501±0.1049, respectively. In the binary classification between two actions, the accuracies of STRCA, CNN, and FBTRCA were 0.5970±0.1424, 0.6112±0.1438, 0.6216±0.1409, respectively. Feature selection using filter banks, as in FBTRCA, improves on the classification performance of STRCA.

SIGNIFICANCE: The proposed method provides a way to select filter banks in pre-movement decoding, and thus it improves the classification performance. The improved pre-movement decoding of single upper limb movements is expected to provide people with severe motor disabilities with a more natural, non-invasive control of their external devices.

RevDate: 2022-11-01

Guo Z, F Chen (2022)

Decoding lexical tones and vowels in imagined tonal monosyllables using fNIRS signals.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Speech is a common way of communication. Decoding verbal intent could provide a naturalistic communication way for people with severe motor disabilities. Active brain computer interaction (BCI) speller is one of the most commonly used speech BCIs. To reduce the spelling time of Chinese words, identifying vowels and tones that are embedded in imagined Chinese words is essential. Functional near-infrared spectroscopy (fNIRS) has been widely used in BCI because it is portable, non-invasive, safe, low cost, and has a relatively high spatial resolution.

APPROACH: In this study, an active BCI speller based on fNIRS is presented by covertly rehearsing tonal monosyllables with vowels (i.e., /a/, /i/, /o/, and /u/) and four lexical tones in Mandarin Chinese (i.e., tones 1, 2, 3, and 4) for 10 s.

MAIN RESULTS: fNIRS results showed significant differences in the right superior temporal gyrus between imagined vowels with tone 2/3/4 and those with tone 1 (i.e., more activations and stronger connections to other brain regions for imagined vowels with tones 2/3/4 than for those with tone 1). Speech-related areas for tone imagery (i.e., the right hemisphere) provided majority of information for identifying tones, while the left hemisphere had advantages in vowel identification. Having decoded both vowels and tones during the post-stimulus 15 s period, the average classification accuracies exceeded 40 % and 70 % in multiclass (i.e., four classes) and binary settings, respectively. To spell words more quickly, the time window size for decoding was reduced from 15 s to 2.5 s while the classification accuracies were not significantly reduced.

SIGNIFICANCE: For the first time, this work demonstrated the possibility of discriminating lexical tones and vowels in imagined tonal syllables. In addition, the reduced time window for decoding indicated that the spelling time of Chinese words could be significantly reduced in the fNIRS-based BCIs.

RevDate: 2022-10-31

Phang CR, Chen CH, Cheng YY, et al (2022)

Frontoparietal Dysconnection in Covert Bipedal Activity for Enhancing the Performance of the Motor Preparation-based Brain-Computer Interface.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Motor-based brain-computer interfaces (BCIs) were developed from the brain signals during motor imagery (MI), motor preparation (MP), and motor execution (ME). Motor-based BCIs provide an active rehabilitation scheme for post-stroke patients. However, BCI based solely on MP was rarely investigated. Since MP is the precedence phase before MI or ME, MP-BCI could potentially detect brain commands at an earlier state. This study proposes a bipedal MP-BCI system, which is actuated by the reduction in frontoparietal connectivity strength. Three substudies, including bipedal classification, neurofeedback, and post-stroke analysis, were performed to validate the performance of our proposed model. In bipedal classification, functional connectivity was extracted by Pearson's correlation model from electroencephalogram (EEG) signals recorded while the subjects were performing MP and MI. The binary classification of MP achieved short-lived peak accuracy of 73.73(±7.99)% around 200-400 ms post-cue. The peak accuracy was found synchronized to the MP-related potential and the decrement in frontoparietal connection strength. The connection strengths of the right frontal and left parietal lobes in the alpha range were found negatively correlated to the classification accuracy. In the subjective neurofeedback study, the majority of subjects reported that motor preparation instead of the motor imagery activated the frontoparietal dysconnection. Post-stroke study also showed that patients exhibit lower frontoparietal connections compared to healthy subjects during both MP and ME phases. These findings suggest that MP reduced alpha band functional frontoparietal connectivity and the EEG signatures of left and right foot MP could be discriminated more effectively during this phase. A neurofeedback paradigm based on the frontoparietal network could also be utilized to evaluate post-stroke rehabilitation training.

RevDate: 2022-10-31

Gao Y, Liu Y, She Q, et al (2022)

Domain Adaptive Algorithm Based on Multi-manifold Embedded Distributed Alignment for Brain-Computer Interfaces.

IEEE journal of biomedical and health informatics, PP: [Epub ahead of print].

The use of transfer learning in brain-computer interfaces (BCIs) has potential applications. As electroencephalogram (EEG) signals vary among different paradigms and subjects, existing EEG transfer learning algorithms mainly focus on the alignment of the original space. They may not discover hidden details owing to the low-dimensional structure of EEG. To effectively transfer data from a source to target domain, a multi-manifold embedding domain adaptive algorithm is proposed for BCI. First, we aligned the EEG covariance matrix in the Riemannian manifold and extracted the characteristics of each source domain in the tangent space to reflect the differences between different source domains. Subsequently, we mapped the extracted characteristics to the Grassmann manifold to obtain a common feature representation. In domain adaptation, the geometric and statistical attributes of EEG data were considered simultaneously, and the target domain divergence matrix was updated with pseudo-labels to maximize the inter-class distance and minimize the intra-class distance. Datasets generated via BCIs were used to verify the effectiveness of the algorithm. Under two experimental paradigms, namely single-source to single-target and multi-source to single-target, the average accuracy of the algorithm on three datasets was 73.31% and 81.02%, respectively, which is more than that of several state-of-the-art EEG cross-domain classification approaches. Our multi-manifold embedded domain adaptive method achieved satisfactory results on EEG transfer learning. The method can achieve effective EEG classification without a same subject's training set.

RevDate: 2022-10-31

Cui Y, Xie S, Xie X, et al (2022)

Dynamic probability integration for electroencephalography-based rapid serial visual presentation performance enhancement: Application in nighttime vehicle detection.

Frontiers in computational neuroscience, 16:1006361.

Background: Rapid serial visual presentation (RSVP) has become a popular target detection method by decoding electroencephalography (EEG) signals, owing to its sensitivity and effectiveness. Most current research on EEG-based RSVP tasks focused on feature extraction algorithms developed to deal with the non-stationarity and low signal-to-noise ratio (SNR) of EEG signals. However, these algorithms cannot handle the problem of no event-related potentials (ERP) component or miniature ERP components caused by the attention lapses of human vision in abnormal conditions. The fusion of human-computer vision can obtain complementary information, making it a promising way to become an efficient and general way to detect objects, especially in attention lapses.

Methods: Dynamic probability integration (DPI) was proposed in this study to fuse human vision and computer vision. A novel basic probability assignment (BPA) method was included, which can fully consider the classification capabilities of different heterogeneous information sources for targets and non-targets and constructs the detection performance model for the weight generation based on classification capabilities. Furthermore, a spatial-temporal hybrid common spatial pattern-principal component analysis (STHCP) algorithm was designed to decode EEG signals in the RSVP task. It is a simple and effective method of distinguishing target and non-target using spatial-temporal features.

Results: A nighttime vehicle detection based on the RSVP task was performed to evaluate the performance of DPI and STHCP, which is one of the conditions of attention lapses because of its decrease in visual information. The average AUC of DPI was 0.912 ± 0.041 and increased by 11.5, 5.2, 3.4, and 1.7% compared with human vision, computer vision, naive Bayesian fusion, and dynamic belief fusion (DBF), respectively. A higher average balanced accuracy of 0.845 ± 0.052 was also achieved using DPI, representing that DPI has the balanced detection capacity of target and non-target. Moreover, STHCP obtained the highest AUC of 0.818 ± 0.06 compared with the other two baseline methods and increased by 15.4 and 23.4%.

Conclusion: Experimental results indicated that the average AUC and balanced accuracy of the proposed fusion method were higher than individual detection methods used for fusion, as well as two excellent fusion methods. It is a promising way to improve detection performance in RSVP tasks, even in abnormal conditions.

RevDate: 2022-10-31

Ma T, Li Y, Huggins JE, et al (2022)

Bayesian Inferences on Neural Activity in EEG-Based Brain-Computer Interface.

Journal of the American Statistical Association, 117(539):1122-1133.

A brain-computer interface (BCI) is a system that translates brain activity into commands to operate technology. A common design for an electroencephalogram (EEG) BCI relies on the classification of the P300 event-related potential (ERP), which is a response elicited by the rare occurrence of target stimuli among common non-target stimuli. Few existing ERP classifiers directly explore the underlying mechanism of the neural activity. To this end, we perform a novel Bayesian analysis of the probability distribution of multi-channel real EEG signals under the P300 ERP-BCI design. We aim to identify relevant spatial temporal differences of the neural activity, which provides statistical evidence of P300 ERP responses and helps design individually efficient and accurate BCIs. As one key finding of our single participant analysis, there is a 90% posterior probability that the target ERPs of the channels around visual cortex reach their negative peaks around 200 milliseconds post-stimulus. Our analysis identifies five important channels (PO7, PO8, Oz, P4, Cz) for the BCI speller leading to a 100% prediction accuracy. From the analyses of nine other participants, we consistently select the identified five channels, and the selection frequencies are robust to small variations of bandpass filters and kernel hyper-parameters.

RevDate: 2022-10-31

Lim J, Wang PT, Shaw SJ, et al (2022)

Artifact propagation in subdural cortical electrostimulation: Characterization and modeling.

Frontiers in neuroscience, 16:1021097.

Cortical stimulation via electrocorticography (ECoG) may be an effective method for inducing artificial sensation in bi-directional brain-computer interfaces (BD-BCIs). However, strong electrical artifacts caused by electrostimulation may significantly degrade or obscure neural information. A detailed understanding of stimulation artifact propagation through relevant tissues may improve existing artifact suppression techniques or inspire the development of novel artifact mitigation strategies. Our work thus seeks to comprehensively characterize and model the propagation of artifacts in subdural ECoG stimulation. To this end, we collected and analyzed data from eloquent cortex mapping procedures of four subjects with epilepsy who were implanted with subdural ECoG electrodes. From this data, we observed that artifacts exhibited phase-locking and ratcheting characteristics in the time domain across all subjects. In the frequency domain, stimulation caused broadband power increases, as well as power bursts at the fundamental stimulation frequency and its super-harmonics. The spatial distribution of artifacts followed the potential distribution of an electric dipole with a median goodness-of-fit of R 2 = 0.80 across all subjects and stimulation channels. Artifacts as large as ±1,100 μV appeared anywhere from 4.43 to 38.34 mm from the stimulation channel. These temporal, spectral and spatial characteristics can be utilized to improve existing artifact suppression techniques, inspire new strategies for artifact mitigation, and aid in the development of novel cortical stimulation protocols. Taken together, these findings deepen our understanding of cortical electrostimulation and provide critical design specifications for future BD-BCI systems.

RevDate: 2022-10-31

Li M, Gong A, Nan W, et al (2022)

[Neurofeedback technology based on functional near infrared spectroscopy imaging and its applications].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 39(5):1041-1049.

Neurofeedback (NF) technology based on electroencephalogram (EEG) data or functional magnetic resonance imaging (fMRI) has been widely studied and applied. In contrast, functional near infrared spectroscopy (fNIRS) has become a new technique in NF research in recent years. fNIRS is a neuroimaging technology based on hemodynamics, which has the advantages of low cost, good portability and high spatial resolution, and is more suitable for use in natural environments. At present, there is a lack of comprehensive review on fNIRS-NF technology (fNIRS-NF) in China. In order to provide a reference for the research of fNIRS-NF technology, this paper first describes the principle, key technologies and applications of fNIRS-NF, and focuses on the application of fNIRS-NF. Finally, the future development trend of fNIRS-NF is prospected and summarized. In conclusion, this paper summarizes fNIRS-NF technology and its application, and concludes that fNIRS-NF technology has potential practicability in neurological diseases and related fields. fNIRS can be used as a good method for NF training. This paper is expected to provide reference information for the development of fNIRS-NF technology.

RevDate: 2022-10-31

Cao H, Jung TP, Chen Y, et al (2022)

[Research advances in non-invasive brain-computer interface control strategies].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 39(5):1033-1040.

Brain-computer interface (BCI) can establish a direct communications pathway between the human brain and the external devices, which is independent of peripheral nerves and muscles. Compared with invasive BCI, non-invasive BCI has the advantages of low cost, low risk, and ease of operation. In recent years, using non-invasive BCI technology to control devices has gradually evolved into a new type of human-computer interaction manner. Moreover, the control strategy for BCI is an essential component of this manner. First, this study introduced how the brain control techniques were developed and classified. Second, the basic characteristics of direct and shared control strategies were thoroughly explained. And then the benefits and drawbacks of these two strategies were compared and further analyzed. Finally, the development direction and application prospects for non-invasive brain control strategies were suggested.

RevDate: 2022-10-30

Naik AG, Kenyon RV, Taheri A, et al (2022)

V-NeuroStack: Open-source 3D time stack software for identifying patterns in neuronal data.

Journal of neuroscience research [Epub ahead of print].

Understanding functional correlations between the activities of neuron populations is vital for the analysis of neuronal networks. Analyzing large-scale neuroimaging data obtained from hundreds of neurons simultaneously poses significant visualization challenges. We developed V-NeuroStack, a novel network visualization tool to visualize data obtained using calcium imaging of spontaneous activity of neurons in a mouse brain slice as well as in vivo using two-photon imaging. V-NeuroStack creates 3D time stacks by stacking 2D time frames for a time-series dataset. It provides a web interface to explore and analyze data using both 3D and 2D visualization techniques. Previous attempts to analyze such data have been limited by the tools available to visualize large numbers of correlated activity traces. V-NeuroStack's 3D view is used to explore patterns in dynamic large-scale correlations between neurons over time. The 2D view is used to examine any timestep of interest in greater detail. Furthermore, a dual-line graph provides the ability to explore the raw and first-derivative values of activity from an individual or a functional cluster of neurons. V-NeuroStack can scale to datasets with at least a few thousand temporal snapshots. It can potentially support future advancements in in vitro and in vivo data capturing techniques to bring forth novel hypotheses by allowing unambiguous visualization of massive patterns in neuronal activity data.

RevDate: 2022-10-28

Scott CJ, de Mestre AM, Verheyen KL, et al (2022)

Bayesian accuracy estimates and fit for purpose thresholds of cytology and culture of endometrial swab samples for detecting endometritis in mares.

Preventive veterinary medicine, 209:105783 pii:S0167-5877(22)00216-1 [Epub ahead of print].

The overall aim of this work was to identify the potential impact of misclassification errors associated with routine screening and diagnostic testing for endometritis in mares. Using Bayesian latent class models (BLCM), specific objectives were to: 1) estimate the diagnostic accuracy of cytology and culture of endometrial swab samples to detect endometritis in mares; 2) assess the impact of different cytology thresholds on test accuracy and misclassification costs; and 3) assess the sensitivity (Se) and specificity (Sp) of a diagnostic strategy including both tests interpreted in series and parallel. Diagnostic and pre-breeding endometrial swab samples collected from 3448 mares based at breeding premises located in the South East of England between 2014 and 2020 were retrospectively analysed. Culture results were classified as positive according to three different case definitions: (A) > 90% of the growth colonies were a monoculture; (B) pathogenic or pathogenic and non-pathogenic bacteria were identified; and (C) any growth was observed. Endometrial smears were graded based on the percent of polymorphonuclear cells (PMN) per high power field (HPF). A hierarchical BLCM was fitted using the cross-tabulated results of the three culture case definitions with a cytology threshold fixed at > 0.5% PMN. Fit for purpose cytology thresholds were proposed using a misclassification cost analysis in the context of good antimicrobial stewardship and for varying endometritis prevalence estimates. Median [95% Bayesian credible intervals (BCI)] cytology Se estimates were 6.5% (2.2-11.6), 6.4% (2.2-10.8) and 6.3% (2.2-10.8) for scenario A, B and C, respectively. Median (95% BCI) cytology Sp estimates were 88.8% (83.1-94.8), 88.9% (83.9-93.8) and 88.8% (84.0-93.8) for scenarios A, B and C, respectively. Median (95% BCI) culture Se estimates were 37.5% (29.9-46.0), 42.3% (33.8-51.1) and 46.4% (35.7-55.9) for scenarios A, B and C, respectively. Median (95% BCI) culture Sp estimates were 92.8% (84.3-99.0), 91.5% (82.5-98.0) and 90.8% (80.1-97.4) for scenarios A, B and C, respectively. Regardless of the culture case definition, Se and Sp of cytology (> 0.5% PMN) was lower than previously reported for swab samples in studies using histology as the reference standard test. The misclassification cost term decreased as the cytology threshold increased for all scenarios and all prevalence contexts, suggesting that, regardless of the endometritis prevalence in the population, increasing the cytology threshold would reduce the misclassification costs associated with false positive mares contributing to good antimicrobial stewardship.

RevDate: 2022-10-28

Lee T, Nam S, DJ Hyun (2022)

Adaptive Window Method Based on FBCCA for Optimal SSVEP Recognition.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

In the conventional studies related to steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), the window length (detection time) was typically predetermined through the offline analysis, which had limitations of practical applicability of a BCI system due to the inter-subject/trial variability of electroencephalography (EEG) signals. To address these limitations, this study aims to automatically optimize the window length for each trial based on training-free approaches and proposes a novel adaptive window method (ANCOVA-based filter-bank canonical correlation analysis, ABFCCA) for SSVEP-based BCIs. The proposed method is based on analysis of covariance (ANCOVA) which is applied after feature extraction by the conventional training-free SSVEP recognition approaches. To evaluate the performance of the proposed method, conventional fixed window and recent adaptive window methods were compared using two open-access datasets. In the Benchmark dataset, the average information transfer rate (ITR) was 146.81 bits/min, the average accuracy 93.55%, and the average window length 1.53 s. In the OpenBMI dataset, the average ITR was 119.01 bits/min, the average accuracy 83.50%, and the average window length 0.65 s. The proposed method significantly outperformed the conventional approaches with fixed window in terms of the accuracy and ITR, and is applicable to various SSVEP-based BCI paradigms based on the criterion of significance level without offline analysis to find optimal hyper-parameters. ABFCCA is enabled the practical use of various BCI systems by automatically optimizing the window length independently.

RevDate: 2022-10-29

Valeriani D, Santoro F, M Ienca (2022)

The present and future of neural interfaces.

Frontiers in neurorobotics, 16:953968.

The 2020's decade will likely witness an unprecedented development and deployment of neurotechnologies for human rehabilitation, personalized use, and cognitive or other enhancement. New materials and algorithms are already enabling active brain monitoring and are allowing the development of biohybrid and neuromorphic systems that can adapt to the brain. Novel brain-computer interfaces (BCIs) have been proposed to tackle a variety of enhancement and therapeutic challenges, from improving decision-making to modulating mood disorders. While these BCIs have generally been developed in an open-loop modality to optimize their internal neural decoders, this decade will increasingly witness their validation in closed-loop systems that are able to continuously adapt to the user's mental states. Therefore, a proactive ethical approach is needed to ensure that these new technological developments go hand in hand with the development of a sound ethical framework. In this perspective article, we summarize recent developments in neural interfaces, ranging from neurohybrid synapses to closed-loop BCIs, and thereby identify the most promising macro-trends in BCI research, such as simulating vs. interfacing the brain, brain recording vs. brain stimulation, and hardware vs. software technology. Particular attention is devoted to central nervous system interfaces, especially those with application in healthcare and human enhancement. Finally, we critically assess the possible futures of neural interfacing and analyze the short- and long-term implications of such neurotechnologies.

RevDate: 2022-10-29

Liu S (2022)

Applying antagonistic activation pattern to the single-trial classification of mental arithmetic.

Heliyon, 8(10):e11102.

Background: At present, the application of fNIRS in the field of brain-computer interface (BCI) is being a hot topic. By fNIRS-BCI, the brain realizes the control of external devices. A state-of-the-art BCI system has five steps which are cerebral cortex signal acquisition, data pre-processing, feature selection and extraction, feature classification and application interface. Proper feature selection and extraction are crucial to the final fNIRS-BCI effect. This paper proposes a feature selection and extraction method for the mental arithmetic task. Specifically, we modified the antagonistic activation pattern approach and used the combination of antagonistic activation patterns to extract features for enhancement of the classification accuracy with low calculation costs.

Methods: Experiments are conducted on an open-acquisition dataset including fNIRS signals of eight healthy subjects of mental arithmetic (MA) tasks and rest tasks. First, the signals are filtered using band-pass filters to remove noise. Second, channels are selected by prior knowledge about antagonistic activation patterns. We used cerebral blood volume (CBV) and cerebral oxygen exchange (COE) of selected each channel to build novel attributes. Finally, we proposed three groups of attributes which are CBV, COE and CBV + COE. Based on attributes generated by the proposed method, we calculated temporal statistical measures (average, variance, maximum, minimum and slope). Any two of five statistical measures were combined as feature sets.

Main results: With the LDA, QDA, and SVM classifiers, the proposed method obtained higher classification accuracies the basic control method. The maximum classification accuracies achieved by the proposed method are 67.45 ± 14.56% with LDA classifier, 89.73 ± 5.71% with QDA classifier, and 87.04 ± 6.88% with SVM classifier. The novel method reduced the running time by 3.75 times compared with the method incorporating all channels into the feature set. Therefore, the novel method reduces the computational costs while maintaining high classification accuracy. The results are validated by another open-access dataset including MA and rest tasks of 29 healthy subjects.

RevDate: 2022-10-27

Zhang Y, Liu D, Zhang P, et al (2022)

Combining robust level extraction and unsupervised adaptive classification for high-accuracy fNIRS-BCI: An evidence on single-trial differentiation between mentally arithmetic- and singing-tasks.

Frontiers in neuroscience, 16:938518.

Functional near-infrared spectroscopy (fNIRS) is a safe and non-invasive optical imaging technique that is being increasingly used in brain-computer interfaces (BCIs) to recognize mental tasks. Unlike electroencephalography (EEG) which directly measures neural activation, fNIRS signals reflect neurovascular-coupling inducing hemodynamic response that can be slow in time and varying in the pattern. The established classifiers extend the EEG-ones by mostly employing the feature based supervised models such as the support vector machine (SVM) and linear discriminant analysis (LDA), and fail to timely characterize the level-sensitive hemodynamic pattern. A dedicated classifier is desired for intentional activity recognition of fNIRS-BCI, including the adaptive acquisition of response relevant features and accurate discrimination of implied ideas. To this end, we herein propose a specifically-designed joint adaptive classification method that combines a Kalman filtering (KF) for robust level extraction and an adaptive Gaussian mixture model (a-GMM) for enhanced pattern recognition. The simulative investigations and paradigm experiments have shown that the proposed KF/a-GMM classification method can effectively track the random variations of task-evoked brain activation patterns, and improve the accuracy of single-trial classification task of mental arithmetic vs. mental singing, as compared to the conventional methods, e.g., those that employ combinations of the band-pass filtering (BPF) based feature extractors (mean, slope, and variance, etc.) and the classical recognizers (GMM, SVM, and LDA). The proposed approach paves a promising way for developing the real-time fNIRS-BCI technique.

RevDate: 2022-10-27

Xu Y, Yin H, Yi W, et al (2022)

Supervised and Semisupervised Manifold Embedded Knowledge Transfer in Motor Imagery-Based BCI.

Computational intelligence and neuroscience, 2022:1603104.

A long calibration procedure limits the use in practice for a motor imagery (MI)-based brain-computer interface (BCI) system. To tackle this problem, we consider supervised and semisupervised transfer learning. However, it is a challenge for them to cope with high intersession/subject variability in the MI electroencephalographic (EEG) signals. Based on the framework of unsupervised manifold embedded knowledge transfer (MEKT), we propose a supervised MEKT algorithm (sMEKT) and a semisupervised MEKT algorithm (ssMEKT), respectively. sMEKT only has limited labelled samples from a target subject and abundant labelled samples from multiple source subjects. Compared to sMEKT, ssMEKT adds comparably more unlabelled samples from the target subject. After performing Riemannian alignment (RA) and tangent space mapping (TSM), both sMEKT and ssMEKT execute domain adaptation to shorten the differences among subjects. During domain adaptation, to make use of the available samples, two algorithms preserve the source domain discriminability, and ssMEKT preserves the geometric structure embedded in the labelled and unlabelled target domains. Moreover, to obtain a subject-specific classifier, sMEKT minimizes the joint probability distribution shift between the labelled target and source domains, whereas ssMEKT performs the joint probability distribution shift minimization between the unlabelled target domain and all labelled domains. Experimental results on two publicly available MI datasets demonstrate that our algorithms outperform the six competing algorithms, where the sizes of labelled and unlabelled target domains are variable. Especially for the target subjects with 10 labelled samples and 270/190 unlabelled samples, ssMEKT shows 5.27% and 2.69% increase in average accuracy on the two abovementioned datasets compared to the previous best semisupervised transfer learning algorithm (RA-regularized common spatial patterns-weighted adaptation regularization, RA-RCSP-wAR), respectively. Therefore, our algorithms can effectively reduce the need of labelled samples for the target subject, which is of importance for the MI-based BCI application.

RevDate: 2022-10-27

Ng CR, Fiedler P, Kuhlmann L, et al (2022)

Multi-Center Evaluation of Gel-Based and Dry Multipin EEG Caps.

Sensors (Basel, Switzerland), 22(20): pii:s22208079.

Dry electrodes for electroencephalography (EEG) allow new fields of application, including telemedicine, mobile EEG, emergency EEG, and long-term repetitive measurements for research, neurofeedback, or brain-computer interfaces. Different dry electrode technologies have been proposed and validated in comparison to conventional gel-based electrodes. Most previous studies have been performed at a single center and by single operators. We conducted a multi-center and multi-operator study validating multipin dry electrodes to study the reproducibility and generalizability of their performance in different environments and for different operators. Moreover, we aimed to study the interrelation of operator experience, preparation time, and wearing comfort on the EEG signal quality. EEG acquisitions using dry and gel-based EEG caps were carried out in 6 different countries with 115 volunteers, recording electrode-skin impedances, resting state EEG and evoked activity. The dry cap showed average channel reliability of 81% but higher average impedances than the gel-based cap. However, the dry EEG caps required 62% less preparation time. No statistical differences were observed between the gel-based and dry EEG signal characteristics in all signal metrics. We conclude that the performance of the dry multipin electrodes is highly reproducible, whereas the primary influences on channel reliability and signal quality are operator skill and experience.

RevDate: 2022-10-27

Oikonomou VP (2022)

An Adaptive Task-Related Component Analysis Method for SSVEP Recognition.

Sensors (Basel, Switzerland), 22(20): pii:s22207715.

Steady-State Visual Evoked Potential (SSVEP) recognition methods use a subject's calibration data to differentiate between brain responses, hence, providing the SSVEP-based brain-computer interfaces (BCIs) with high performance. However, they require sufficient calibration EEG trials to achieve that. This study develops a new method to learn from limited calibration EEG trials, and it proposes and evaluates a novel adaptive data-driven spatial filtering approach for enhancing SSVEP detection. The spatial filter learned from each stimulus utilizes temporal information from the corresponding EEG trials. To introduce the temporal information into the overall procedure, a multitask learning approach, based on the Bayesian framework, is adopted. The performance of the proposed method was evaluated into two publicly available benchmark datasets, and the results demonstrated that our method outperformed competing methods by a significant margin.

RevDate: 2022-10-27

Liu N, Wang H, Wang S, et al (2022)

Liquid Oxygen Compatibility and Ultra-Low-Temperature Mechanical Properties of Modified epoxy Resin Containing Phosphorus and Nitrogen.

Polymers, 14(20): pii:polym14204343.

Endowing epoxy resin (EP) with prospective liquid oxygen compatibility (LOC) as well as enhanced ultra-low-temperature mechanical properties is urgently required in order to broaden its applications in aerospace engineering. In this study, a reactive phosphorus/nitrogen-containing aromatic ethylenediamine (BSEA) was introduced as a reactive component to enhance the LOC and ultra-low-temperature mechanical properties of an EP/biscitraconimide resin (BCI) system. The resultant EP thermosets showed no sensitivity reactions in the 98J liquid oxygen impact test (LOT) when the BSEA content reached 4 wt% or 5 wt%, indicating that they were compatible with liquid oxygen. Moreover, the bending properties, fracture toughness and impact strength of BSEA-modified EP were greatly enhanced at RT and cryogenic temperatures (77 K) at an appropriate level of BSEA content. The bending strength (251.64 MPa) increased by 113.67%, the fracture toughness (2.97 MPa·m1/2) increased by 81.10%, and the impact strength (31.85 kJ·m-2) increased by 128.81% compared with that of pure EP at 77 K. All the above results demonstrate that the BSEA exhibits broad application potential in liquid oxygen tanks and in the cryogenic field.

RevDate: 2022-10-27

Fareed MM, Qasmi M, Aziz S, et al (2022)

The Role of Clusterin Transporter in the Pathogenesis of Alzheimer's Disease at the Blood-Brain Barrier Interface: A Systematic Review.

Biomolecules, 12(10): pii:biom12101452.

Alzheimer's disease (AD) is considered a chronic and debilitating neurological illness that is increasingly impacting older-age populations. Some proteins, including clusterin (CLU or apolipoprotein J) transporter, can be linked to AD, causing oxidative stress. Therefore, its activity can affect various functions involving complement system inactivation, lipid transport, chaperone activity, neuronal transmission, and cellular survival pathways. This transporter is known to bind to the amyloid beta (Aβ) peptide, which is the major pathogenic factor of AD. On the other hand, this transporter is also active at the blood-brain barrier (BBB), a barrier that prevents harmful substances from entering and exiting the brain. Therefore, in this review, we discuss and emphasize the role of the CLU transporter and CLU-linked molecular mechanisms at the BBB interface in the pathogenesis of AD.

RevDate: 2022-10-27

Ning Y, Wan G, Liu T, et al (2022)

Volitional Generation of Reproducible, Efficient Temporal Patterns.

Brain sciences, 12(10): pii:brainsci12101269.

One of the extraordinary characteristics of the biological brain is the low energy expense it requires to implement a variety of biological functions and intelligence as compared to the modern artificial intelligence (AI). Spike-based energy-efficient temporal codes have long been suggested as a contributor for the brain to run on low energy expense. Despite this code having been largely reported in the sensory cortex, whether this code can be implemented in other brain areas to serve broader functions and how it evolves throughout learning have remained unaddressed. In this study, we designed a novel brain-machine interface (BMI) paradigm. Two macaques could volitionally generate reproducible energy-efficient temporal patterns in the primary motor cortex (M1) by learning the BMI paradigm. Moreover, most neurons that were not directly assigned to control the BMI did not boost their excitability, and they demonstrated an overall energy-efficient manner in performing the task. Over the course of learning, we found that the firing rates and temporal precision of selected neurons co-evolved to generate the energy-efficient temporal patterns, suggesting that a cohesive rather than dissociable processing underlies the refinement of energy-efficient temporal patterns.

RevDate: 2022-10-27

Filho G, Júnior C, Spinelli B, et al (2022)

All-Polymeric Electrode Based on PEDOT:PSS for In Vivo Neural Recording.

Biosensors, 12(10): pii:bios12100853.

One of the significant challenges today in the brain-machine interfaces that use invasive methods is the stability of the chronic record. In recent years, polymer-based electrodes have gained notoriety for achieving mechanical strength values close to that of brain tissue, promoting a lower immune response to the implant. In this work, we fabricated fully polymeric electrodes based on PEDOT:PSS for neural recording in Wistar rats. We characterized the electrical properties and both in vitro and in vivo functionality of the electrodes. Additionally, we employed histological processing and microscopical visualization to evaluate the tecidual immune response at 7, 14, and 21 days post-implant. Electrodes with 400-micrometer channels showed a 12 dB signal-to-noise ratio. Local field potentials were characterized under two conditions: anesthetized and free-moving. There was a proliferation of microglia at the tissue-electrode interface in the early days, though there was a decrease after 14 days. Astrocytes also migrated to the interface, but there was not continuous recruitment of these cells in the tissue; there was inflammatory stability by day 21. The signal was not affected by this inflammatory action, demonstrating that fully polymeric electrodes can be an alternative means to prolong the valuable time of neural recordings.

RevDate: 2022-10-27

Chen W, Chen SK, Liu YH, et al (2022)

An Electric Wheelchair Manipulating System Using SSVEP-Based BCI System.

Biosensors, 12(10): pii:bios12100772.

Most people with motor disabilities use a joystick to control an electric wheelchair. However, those who suffer from multiple sclerosis or amyotrophic lateral sclerosis may require other methods to control an electric wheelchair. This study implements an electroencephalography (EEG)-based brain-computer interface (BCI) system and a steady-state visual evoked potential (SSVEP) to manipulate an electric wheelchair. While operating the human-machine interface, three types of SSVEP scenarios involving a real-time virtual stimulus are displayed on a monitor or mixed reality (MR) goggles to produce the EEG signals. Canonical correlation analysis (CCA) is used to classify the EEG signals into the corresponding class of command and the information transfer rate (ITR) is used to determine the effect. The experimental results show that the proposed SSVEP stimulus generates the EEG signals because of the high classification accuracy of CCA. This is used to control an electric wheelchair along a specific path. Simultaneous localization and mapping (SLAM) is the mapping method that is available in the robotic operating software (ROS) platform that is used for the wheelchair system for this study.

RevDate: 2022-10-26

Bak S, Jeong Y, Yeu M, et al (2022)

Brain-computer interface to predict impulse buying behavior using functional near-infrared spectroscopy.

Scientific reports, 12(1):18024.

As the rate of vaccination against COVID-19 is increasing, demand for overseas travel is also increasing. Despite people's preference for duty-free shopping, previous studies reported that duty-free shopping increases impulse buying behavior. There are also self-reported tools to measure their impulse buying behavior, but it has the disadvantage of relying on the human memory and perception. Therefore, we propose a Brain-Computer Interface (BCI)-based brain signal processing methodology to supplement these limitations and to reduce ambiguity and conjecture of data. To achieve this goal, we focused on the brain's prefrontal cortex (PFC) activity, which supervises human decision-making and is closely related to impulse buying behavior. The PFC activation is observed by recording signals using a functional near-infrared spectroscopy (fNIRS) while inducing impulse buying behavior in virtual computing environments. We found that impulse buying behaviors were not only higher in online duty-free shops than in online regular stores, but the fNIRS signals were also different on the two sites. We also achieved an average accuracy of 93.78% in detecting impulse buying patterns using a support vector machine. These results were identical to the people's self-reported responses. This study provides evidence as a potential biomarker for detecting impulse buying behavior with fNIRS.

RevDate: 2022-10-26

An KM, Shim JH, Kwon H, et al (2022)

Detection of the 40 Hz auditory steady-state response with optically pumped magnetometers.

Scientific reports, 12(1):17993.

Magnetoencephalography (MEG) is a functional neuroimaging technique that noninvasively detects the brain magnetic field from neuronal activations. Conventional MEG measures brain signals using superconducting quantum interference devices (SQUIDs). SQUID-MEG requires a cryogenic environment involving a bulky non-magnetic Dewar flask and the consumption of liquid helium, which restricts the variability of the sensor array and the gap between the cortical sources and sensors. Recently, miniature optically pumped magnetometers (OPMs) have been developed and commercialized. OPMs do not require cryogenic cooling and can be placed within millimeters from the scalp. In the present study, we arranged six OPM sensors on the temporal area to detect auditory-related brain responses in a two-layer magnetically shielded room. We presented the auditory stimuli of 1 kHz pure-tone bursts with 200 ms duration and obtained the M50 and M100 components of auditory-evoked fields. We delivered the periodic stimuli with a 40 Hz repetition rate and observed the gamma-band power changes and inter-trial phase coherence of auditory steady-state responses at 40 Hz. We found that the OPM sensors have a performance comparable to that of conventional SQUID-MEG sensors, and our results suggest the feasibility of using OPM sensors for functional neuroimaging and brain-computer interface applications.

RevDate: 2022-10-27
CmpDate: 2022-10-27

Meneses BP, A Amadon (2022)

Physical limits to human brain B0 shimming with spherical harmonics, engineering implications thereof.

Magma (New York, N.Y.), 35(6):923-941.

OBJECTIVE: As the MRI main magnetic field rises for improved signal-to-noise ratio, susceptibility-induced B0-inhomogeneity increases proportionally, aggravating related artifacts. Considering only susceptibility disparities between air and biological tissue, we explore the topological conditions for which perfect shimming could be performed in a Region of Interest (ROI) such as the human brain or part thereof.

MATERIALS AND METHODS: After theoretical considerations for perfect shimming, spherical harmonic (SH) shimming simulations of very high degree are performed, based on a 100-subject database of 1.7-mm-resolved brain fieldmaps acquired at 3T . In addition to the whole brain, shimmed ROIs include slabs targeting the prefrontal cortex, both or single temporal lobes, or spheres in the frontal brain above the nasal sinus.

RESULTS AND DISCUSSION: We show "perfect" SH shimming is possible only if the ROI can be contained in a sphere that does not enclose sources of magnetic field inhomogeneity, which are gathered at the air-tissue interface. We establish a [Formula: see text]Hz inhomogeneity hard shim limit at 7T for whole brain SH shimming, that can only be attained at shimming degree higher than 90. On the other hand, under limited power and SH degree resources, 3D region-specific shimming is shown to greatly improve homogeneity in critical zones such as the prefrontal cortex and around ear canals.

RevDate: 2022-10-26

Kalafatovich J, Lee M, SW Lee (2022)

Learning Spatiotemporal Graph Representations for Visual Perception using EEG Signals.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Perceiving and recognizing objects enable interaction with the external environment. Recently, decoding brain signals based on brain-computer interface (BCI) that recognize the user's intentions by just looking at objects has attracted attention as a next-generation intuitive interface. However, classifying signals from different objects is very challenging, and in practice, decoding performance for visual perception is not yet high enough to be used in real environments. In this study, we aimed to classify single-trial electroencephalography signals evoked by visual stimuli into their corresponding semantic category. We proposed a two-stream convolutional neural network to increase classification performance. The model consists of a spatial stream and a temporal stream that use graph convolutional neural network and channel-wise convolutional neural network respectively. Two public datasets were used to evaluate the proposed model; (i) SU DB (a set of 72 photographs of objects belonging to 6 semantic categories) and MPI DB (8 exemplars belonging to two categories). Our results outperform state-of-the-art methods, with accuracies of 54.28 ± 7.89% for SU DB (6-class) and 84.40 ± 8.03% for MPI DB (2-class). These results could facilitate the application of intuitive BCI systems based on visual perception.

RevDate: 2022-10-26

Jin J, Qu T, Xu R, et al (2022)

Motor Imagery EEG Classification Based on Riemannian Sparse Optimization and Dempster-Shafer Fusion of Multi-Time-Frequency Patterns.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Motor imagery-based brain-computer interfaces (MI-BCIs) features are generally extracted from a wide fixed frequency band and time window of EEG signal. The performance suffers from individual differences in corresponding time to MI tasks. In order to solve the problem, in this study, we propose a novel method named Riemannian sparse optimization and Dempster-Shafer fusion of multi-time-frequency patterns (RSODSF) to enhance the decoding efficiency. First, we effectively combine the Riemannian geometry of the spatial covariance matrix with sparse optimization to extract more robust and distinct features. Second, the Dempster-Shafer theory is introduced and used to fuse each time window after sparse optimization of Riemannian features. Besides, the probabilistic values of the support vector machine (SVM) are obtained and transformed to effectively fuse multiple classifiers to leverage potential soft information of multiple trained SVM. The openaccess BCI Competition IV dataset IIa and Competition III dataset IIIa are employed to evaluate the performance of the proposed RSODSF. It achieves higher average accuracy (89.7% and 96.8%) than state-of-the-art methods. The improvement over the common spatial patterns (SFBCSP) are respectively 9.9% and 12.4% (p<0.01, paired t-test). These results show that our proposed RSODSF method is a promising candidate for the performance improvement of MI-BCI.

RevDate: 2022-10-26

Henriques DHN, Alves AMH, Kuntze MM, et al (2022)

Effect of dental tissue thickness on the measurement of oxygen saturation by two different pulse oximeters.

Brazilian dental journal, 33(5):26-34.

This study aimed to evaluate the influence of different dental tissue thickness on the measurement of oxygen saturation (SpO2) levels in high (HP) and low (LP) blood perfusion by comparing the values obtained from two different pulse oximeters (POs) - BCI and Sense 10. Thirty freshly extracted human teeth had their crowns interposed between the POs and an optical simulator, which emulated the SpO2 and heart beats per minute (bpm) at HP (100% SpO2/75 bpm) and LP (86% SpO2/75 bpm) modes. Afterwards, the palatine/lingual surfaces of the dental crowns were worn with diamond drills. The reading of SpO2 was performed again using the POs alternately through the buccal surface of each dental crown. Data were analyzed by the Wilcoxon, Mann-Whitney and Kendall Tau-b tests (α=5%). The results showed significant difference at the HP and LP modes in the SpO2 readouts through the different dental thicknesses with the use of BCI, and at the LP mode with the use of Sense 10, which had a significant linear correlation (p<0.0001) and lower SpO2 readout values in relation to the increase of the dental thickness. Irrespective of tooth thickness, Sense 10 had significantly higher readout values (p<0.0001) than BCI at both perfusion modes. The interposition of different thicknesses of enamel and dentin influenced the POs measurement of SpO2, specially at the low perfusion mode. The POs were more accurate in SpO2 measurement when simulated perfusion levels were higher.

RevDate: 2022-10-26

Cavallaro G, Murri A, Nelson E, et al (2022)

The Impact of the COVID-19 Lockdown on Quality of Life in Adult Cochlear Implant Users: A Survey Study.

Audiology research, 12(5):518-526 pii:audiolres12050052.

BACKGROUND: The COVID-19 pandemic rapidly spread through Europe in the first months of 2020. On the 9th of March 2020, the Italian government ordered a national lock-down. The study's objectives were: to investigate the effect of lockdown on CI users; and to detect the difference in the perception of discomfort existing between unilateral cochlear implant (UCI) users and bilateral cochlear implant (BCI) users, due to the lockdown experience.

METHODS: A 17-item, web-based, anonymous online survey was administered to 57 CI users, exploring hearing performance, emotions, practical issues, behavior, and tinnitus. Participation in the study was voluntary.

RESULTS: all CI users obtained an abnormal score in all questionnaire themes. For the emotion theme and the practical issue theme, the age range 61-90 showed a significant difference between UCI and BCI users in favor of BCI users (emotion theme: UCI mean = 3.9, BCI mean = 2.3, p = 0.0138; practical issues: UCI mean = 4, BCI mean = 3, p = 0.0031).

CONCLUSIONS: CI users experienced the lockdown negatively as regards behavior, emotions, hearing performance, and in practical issues. CI subjects with UCI in old age suffered more from the experience of lockdown than subjects with BCI in the same age, with regards to emotions and practical issues.

RevDate: 2022-10-25

Schubert PJ, Dorkenwald S, Januszewski M, et al (2022)

SyConn2: dense synaptic connectivity inference for volume electron microscopy.

Nature methods [Epub ahead of print].

The ability to acquire ever larger datasets of brain tissue using volume electron microscopy leads to an increasing demand for the automated extraction of connectomic information. We introduce SyConn2, an open-source connectome analysis toolkit, which works with both on-site high-performance compute environments and rentable cloud computing clusters. SyConn2 was tested on connectomic datasets with more than 10 million synapses, provides a web-based visualization interface and makes these data amenable to complex anatomical and neuronal connectivity queries.

RevDate: 2022-10-24

Jamil N, Belkacem AN, A Lakas (2022)

On enhancing students' cognitive abilities in online learning using brain activity and eye movements.

Education and information technologies pii:11372 [Epub ahead of print].

The COVID-19 pandemic has interrupted education institutions in over 150 nations, affecting billions of students. Many governments have forced a transition in higher education from in-person to remote learning. After this abrupt, worldwide transition away from the classroom, some question whether online education will continue to grow in acceptance in post-pandemic times. However, new technology, such as the brain-computer interface and eye-tracking, have the potential to improve the remote learning environment, which currently faces several obstacles and deficiencies. Cognitive brain computer interfaces can help us develop a better understanding of brain functions, allowing for the development of more effective learning methodologies and the enhancement of brain-based skills. We carried out a systematic literature review of research on the use of brain computer interfaces and eye-tracking to measure students' cognitive skills during online learning. We found that, because many experimental tasks depend on recorded rather than real-time video, students don't have direct and real-time interaction with their teacher. Further, we found no evidence in any of the reviewed papers for brain-to-brain synchronization during remote learning. This points to a potentially fruitful future application of brain computer interfaces in education, investigating whether the brains of student-teacher pairs who interact with the same course content have increasingly similar brain patterns.

RevDate: 2022-10-24

Blenkmann AO, Solbakk AK, Ivanovic J, et al (2022)

Modeling intracranial electrodes. A simulation platform for the evaluation of localization algorithms.

Frontiers in neuroinformatics, 16:788685.

Introduction: Intracranial electrodes are implanted in patients with drug-resistant epilepsy as part of their pre-surgical evaluation. This allows the investigation of normal and pathological brain functions with excellent spatial and temporal resolution. The spatial resolution relies on methods that precisely localize the implanted electrodes in the cerebral cortex, which is critical for drawing valid inferences about the anatomical localization of brain function. Multiple methods have been developed to localize the electrodes, mainly relying on pre-implantation MRI and post-implantation computer tomography (CT) images. However, they are hard to validate because there is no ground truth data to test them and there is no standard approach to systematically quantify their performance. In other words, their validation lacks standardization. Our work aimed to model intracranial electrode arrays and simulate realistic implantation scenarios, thereby providing localization algorithms with new ways to evaluate and optimize their performance.

Results: We implemented novel methods to model the coordinates of implanted grids, strips, and depth electrodes, as well as the CT artifacts produced by these. We successfully modeled realistic implantation scenarios, including different sizes, inter-electrode distances, and brain areas. In total, ∼3,300 grids and strips were fitted over the brain surface, and ∼850 depth electrode arrays penetrating the cortical tissue were modeled. Realistic CT artifacts were simulated at the electrode locations under 12 different noise levels. Altogether, ∼50,000 thresholded CT artifact arrays were simulated in these scenarios, and validated with real data from 17 patients regarding the coordinates' spatial deformation, and the CT artifacts' shape, intensity distribution, and noise level. Finally, we provide an example of how the simulation platform is used to characterize the performance of two cluster-based localization methods.

Conclusion: We successfully developed the first platform to model implanted intracranial grids, strips, and depth electrodes and realistically simulate thresholded CT artifacts and their noise. These methods provide a basis for developing more complex models, while simulations allow systematic evaluation of the performance of electrode localization techniques. The methods described in this article, and the results obtained from the simulations, are freely available via open repositories. A graphical user interface implementation is also accessible via the open-source iElectrodes toolbox.

RevDate: 2022-10-24

Wang G, M Cerf (2022)

Brain-Computer Interface using neural network and temporal-spectral features.

Frontiers in neuroinformatics, 16:952474.

Brain-Computer Interfaces (BCIs) are increasingly useful for control. Such BCIs can be used to assist individuals who lost mobility or control over their limbs, for recreational purposes such as gaming or semi-autonomous driving, or as an interface toward man-machine integration. Thus far, the performance of algorithms used for thought decoding has been limited. We show that by extracting temporal and spectral features from electroencephalography (EEG) signals and, following, using deep learning neural network to classify those features, one can significantly improve the performance of BCIs in predicting which motor action was imagined by a subject. Our movement prediction algorithm uses Sequential Backward Selection technique to jointly choose temporal and spectral features and a radial basis function neural network for the classification. The method shows an average performance increase of 3.50% compared to state-of-the-art benchmark algorithms. Using two popular public datasets our algorithm reaches 90.08% accuracy (compared to an average benchmark of 79.99%) on the first dataset and 88.74% (average benchmark: 82.01%) on the second dataset. Given the high variability within- and across-subjects in EEG-based action decoding, we suggest that using features from multiple modalities along with neural network classification protocol is likely to increase the performance of BCIs across various tasks.

RevDate: 2022-10-24

Yao S, Zhou W, Hinson R, et al (2022)

Ultrasoft Porous 3D Conductive Dry Electrodes for Electrophysiological Sensing and Myoelectric Control.

Advanced materials technologies, 7(10):.

Biopotential electrodes have found broad applications in health monitoring, human-machine interactions, and rehabilitation. Here, we report the fabrication and applications of ultrasoft breathable dry electrodes that can address several challenges for their long-term wearable applications - skin compatibility, wearability, and long-term stability. The proposed electrodes rely on porous and conductive silver nanowire based nanocomposites as the robust mechanical and electrical interface. The highly conductive and conformable structure eliminates the necessity of conductive gel while establishing a sufficiently low electrode-skin impedance for high-fidelity electrophysiological sensing. The introduction of gas-permeable structures via a simple and scalable method based on sacrificial templates improves breathability and skin compatibility for applications requiring long-term skin contact. Such conformable and breathable dry electrodes allow for efficient and unobtrusive monitoring of heart, muscle, and brain activities. In addition, based on the muscle activities captured by the electrodes and a musculoskeletal model, electromyogram-based neural-machine interfaces were realized, illustrating the great potential for prosthesis control, neurorehabilitation, and virtual reality.

RevDate: 2022-10-24

Gerginov V, Pomponio M, S Knappe (2020)

Scalar Magnetometry Below 100 fT/Hz1/2 in a Microfabricated Cell.

IEEE sensors journal, 20(21):12684-12690.

Zero-field optically-pumped magnetometers are a room-temperature alternative to traditionally used super-conducting sensors detecting extremely weak magnetic fields. They offer certain advantages such as small size, flexible arrangement, reduced sensitivity in ambient fields offering the possibility for telemetry. Devices based on microfabricated technology are nowadays commercially available. The limited dynamic range and vector nature of the zero-field magnetometers restricts their use to environments heavily shielded against magnetic noise. Total-field (or scalar) magnetometers based on microfabricated cells have demonstrated subpicotesla sensitivities only recently. This work demonstrates a scalar magnetometer based on a single optical axis, 18 (3 × 3 × 2) mm3 microfabricated cell, with a noise floor of 70 fT/Hz1/2. The magnetometer operates in a large static magnetic field range, and and is based on a simple optical and electronic configuration that allows the development of dense sensor arrays. Different methods of magnetometer interrogation are demonstrated. The features of this magnetic field sensor hold promise for applications of miniature sensors in nonzero field environments such as unshielded magnetoencephalography (MEG) and brain-computer interfaces (BCI).

RevDate: 2022-10-23

Sinha S, Finazzi-Agrò E, Dmochowski RR, et al (2022)

The bladder contractility and bladder outlet obstruction indices in adult men: Results of a global Delphi consensus study.

Neurourology and urodynamics [Epub ahead of print].

AIMS: This Delphi study was planned to examine global expert consensus with regard to utility, accuracy, and categorization of Bladder Contractility Index (BCI) and Bladder Outlet Obstruction Index (BOOI) and the related evidence.

METHODS: Twenty-eight experts were invited to answer the two-round survey including three foundation questions and 15 survey questions. Consensus was defined as ≥75% agreement. The ordinal scale (0-10) in round 1 was classified into "strongly agree," "agree," "neutral," "disagree," and "strongly disagree" for the final round. A systematic search for evidence was conducted for therapeutic studies that have examined outcome stratified by the indices in men.

RESULTS: Nineteen experts participated in the survey with 100% completion. Consensus was noted with regard to 6 of 19 questions. Experts strongly agreed with utility of quantifying bladder contractility and bladder outflow obstruction with near unanimity regarding the latter. There was consensus that BCI and BOOI were accurate, that BCI was clinically useful, and for defining severe bladder outflow obstruction as BOOI > 80. Systematic search yielded 69 publications (BCI 45; BOOI 50). Most studies examined the indices as a continuous variable or by standard cutoffs (BCI 100, 150; BOOI 20, 40).

CONCLUSION: There is general agreement among experts on need for indices to quantify bladder contractility and bladder outflow obstruction as well as with regard to accuracy and utility of BCI and BOOI indices. Few studies have examined the discriminant power of existing cutoffs or explored new ones. This is an extraordinary knowledge gap in the field of urology.

RevDate: 2022-10-24
CmpDate: 2022-10-24

Shen X, Zhang X, Huang Y, et al (2022)

Intermediate Sensory Feedback Assisted Multi-Step Neural Decoding for Reinforcement Learning Based Brain-Machine Interfaces.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 30:2834-2844.

Reinforcement-learning (RL)-based brain-machine interfaces (BMIs) interpret dynamic neural activity into movement intention without patients' real limb movements, which is promising for clinical applications. A movement task generally requires the subjects to reach the target within one step and rewards the subjects instantaneously. However, a real BMI scenario involves tasks that require multiple steps, during which sensory feedback is provided to indicate the status of the prosthesis, and the reward is only given at the end of the trial. Actually, subjects internally evaluate the sensory feedback to adjust motor activity. Existing RL-BMI tasks have not fully utilized the internal evaluation from the brain upon the sensory feedback to guide the decoder training, and there lacks an effective tool to assign credit for the multi-step decoding task. We propose first to extract intermediate guidance from the medial prefrontal cortex (mPFC) to assist the learning of multi-step decoding in an RL framework. To effectively explore the neural-action mapping in a large state-action space, a temporal difference (TD) method is incorporated into quantized attention-gated kernel reinforcement learning (QAGKRL) to assign the credit over the temporal sequence of movement, but also discriminate spatially in the Reproducing Kernel Hilbert Space (RKHS). We test our approach on the data collected from the primary motor cortex (M1) and the mPFC of rats when they brain control the cursor to reach the target within multiple steps. Compared with the models which only utilize the final reward, the intermediate evaluation interpreted from the mPFC can help improve the prediction accuracy by 10.9% on average across subjects, with faster convergence and more stability. Moreover, our proposed algorithm further increases 18.2% decoding accuracy compared with existing TD-RL methods. The results reveal the possibility of achieving better multi-step decoding performance for more complicated BMI tasks.

RevDate: 2022-10-22

A W, Du F, He Y, et al (2022)

Graphene oxide reinforced hemostasis of gelatin sponge in noncompressible hemorrhage via synergistic effects.

Colloids and surfaces. B, Biointerfaces, 220:112891 pii:S0927-7765(22)00575-6 [Epub ahead of print].

Effective hemostasis for noncompressible bleeding has been a key challenge because of the deep, narrow, and irregular wounds. Swellable gelatin is an available hemostatic material but is limited by weak mechanical strength and slow liquid absorption. Herein, the design of a gelatin and graphene oxide (GO) composite sponge (GP-GO) that possesses stable cross-linked networks and excellent absorbability, is reported. The GP-GOs are constructed via the thermal radical polymerization technique, using methacrylate gelatin (Gel-MA) and poly(ethylene glycol) diacrylate (PEGDA) as the crosslinker, while GO is uniformly fixed in the network via the curing reaction to further strengthen the stability. The optimized GP-GO5 with GO addition (5 wt%) exhibits high porosity (> 90%), distinguished liquid absorption rate (106 ms), rapidly responsive swelling (422% expansion within 10 s), and stable mechanical properties. The addition of GO effectively reinforces coagulation stimulation of GP-GOs though the stimulation of platelets and the enrichment effect at the interface, significantly reducing the blood coagulation index (BCI) (< 17.5%). Hemostatic mechanism study indicated the liquid absorbability of GP-GOs is the critical foundation to trigger the subsequent physical expansion, blood cells enrichment, and coagulation stimulations. Besides, GP-GO5 exhibits excellent biosafety assessed by hemolysis and cytotoxicity. Under the synergistic effects, the biocompatible GP-GO5 showed excellent hemostatic properties in the hemostasis of severe bleeding and noncompressible wounds compared with a pure gelatin sponge (GP) and the commercial hemostatic agent Celox™. This study demonstrated a promising candidate for practical application of noncompressible wound hemostasis.

RevDate: 2022-10-21

Sosulski J, M Tangermann (2022)

Introducing block-Toeplitz covariance matrices to remaster linear discriminant analysis for event-related potential brain-computer interfaces.

Journal of neural engineering [Epub ahead of print].

Objective.Covariance matrices of noisy multichannel electroencephalogram time series data provide essential information for the decoding of brain signals using machine learning methods. However, small datasets and high dimensionality make it hard to estimate these matrices. In brain-computer interfaces (BCI) based on event-related potentials and a linear discriminant analysis (LDA) classifier, the state of the art covariance estimation uses shrinkage regularization. As this is a general covariance regularization approach, we aim at improving LDA further by better exploiting the domain-specific characteristics of the electroencephalogram to regularize the covariance estimates.Approach.We propose to enforce a block-Toeplitz structure for the covariance matrix of the LDA, which implements an assumption of signal stationarity in short time windows for each channel.Main results.An offline re-analysis of data collected from 213 subjects under 13 different event-related potential BCI protocols showed a significantly increased binary classification performance of this `ToeplitzLDA' compared to shrinkage regularized LDA (up to 6 AUC points) and Riemannian classification approaches (up to 2 AUC points). In an unsupervised visual speller application, this improvement would translate to a reduction of spelling errors by 81% on average for 25 subjects. Additionally, aside from lower memory and reduced time complexity for LDA training, ToeplitzLDA proves to be robust against drastic increases of the number of temporal features.Significance.The proposed covariance estimation allows BCI researchers to improve classification rates and reduce calibration times of BCI protocols using event-related potentials and thus support the usability of corresponding applications. Its lower computational and memory needs could make it a valuable algorithm especially for mobile BCIs.

RevDate: 2022-10-21

Liu S, Zhang J, Wang A, et al (2022)

Subject adaptation convolutional neural network for EEG-based motor imagery classification.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Deep transfer learning has been widely used to address the nonstationarity of electroencephalogram (EEG) data during motor imagery (MI) classification. However, previous deep learning approaches suffer from limited classification accuracy because the temporal and spatial features cannot be effectively extracted.

APPROACH: Here, we propose a novel end-to-end deep subject adaptation model (SACNN) to handle the problem of EEG-based MI classification. Our proposed model jointly optimizes three modules, i.e., a feature extractor, a classifier, and a subject adapter. Specifically, the feature extractor simultaneously extracts the temporal and spatial features from the raw EEG data using a parallel multiscale convolution network. In addition, we design a subject adapter to reduce the feature distribution shift between the source and target subjects by using the maximum mean discrepancy (MMD). By minimizing the classification loss and the distribution discrepancy, the model is able to extract the temporal-spatial features to the prediction of a new subject.

MAIN RESULTS: Extensive experiments are carried out on three EEG-based MI datasets, i.e., BCI Competition IV Dataset IIb, BCI Competition III Dataset IVa, and BCI Competition IV Dataset I, and the average accuracy reaches to 86.42%, 81.71% and 79.35% on the three datasets respectively. Furthermore, the statistical analysis also indicates the significant performance improvement of SACNN.

SIGNIFICANCE: This paper reveals the importance of the temporal-spatial features on EEG-based MI classification task. Our proposed SACNN model can make fully use of the temporal-spatial information to achieve the purpose.

RevDate: 2022-10-21

Spencer M, Kameneva T, Grayden DB, et al (2022)

Quantifying visual acuity for pre-clinical testing of visual prostheses.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Visual prostheses currently restore only limited vision. More research and pre-clinical work are required to improve the devices and stimulation strategies that are used to induce neural activity that results in visual perception. Evaluation of candidate strategies and devices requires an objective way to convert measured and modelled patterns of neural activity into a quantitative measure of visual acuity.

APPROACH: This study presents an approach that compares evoked patterns of neural activation with target and reference patterns. A d-prime measure of discriminability determines whether the evoked neural activation pattern is sufficient to discriminate between the target and reference patterns and thus provides a quantified level of visual perception in the clinical Snellen and MAR scales. The magnitude of the resulting value was demonstrated using scaled standardized "C" and "E" optotypes.

MAIN RESULTS: The approach was used to assess the visual acuity provided by two alternative stimulation strategies applied to simulated retinal implants with different electrode pitch configurations and differently sized spreads of neural activity. It was found that when there is substantial overlap in neural activity generated by different electrodes, an estimate of acuity based only upon electrode pitch is incorrect; our proposed method gives an accurate result in both circumstances.

SIGNIFICANCE: Quantification of visual acuity using this approach in pre-clinical development will allow for more rapid and accurate prototyping of improved devices and neural stimulation strategies.

RevDate: 2022-10-21

Sakkalis V, Krana M, Farmaki C, et al (2022)

Augmented Reality driven Steady-State Visual Evoked Potentials for Wheelchair Navigation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Medically oriented Brain Computer Interfaces (BCIs) have been proposed as a promising approach addressed to individuals suffering from severe paralysis. Steady-State Visual Evoked Potentials (SSVEPs) in particular have been proven successful in many different applications, achieving high information throughput with short or even no training. However, efficient electric wheelchair navigation combining high accuracy and comfort is still not demonstrated. In this paper, we propose the use of an SSVEP-based universal control system featuring augmented reality (AR) glasses in an attempt to increase ease of use and patient acceptability without making compromises on BCI performance. The system received positive user-feedback, reaching a mean accuracy of 90%. Merits and pitfalls of the system proposed are also addressed.

RevDate: 2022-10-21

Margenau EL, Wood PB, Brown DJ, et al (2022)

Evaluating Mechanisms of Short-term Woodland Salamander Response to Forest Management.

Environmental management pii:10.1007/s00267-022-01735-3 [Epub ahead of print].

Contemporary forest management often requires meeting diverse ecological objectives including maintaining ecosystem function and promoting biodiversity through timber harvesting. Wildlife are essential in this process by providing ecological services that can facilitate forest resiliency in response to timber harvesting. However, the mechanisms driving species' responses remain ambiguous. The goal of this study was to assess mechanisms influencing eastern red-backed salamander (RBS; Plethodon cinereus) response to overstory cover removal. We evaluated two mitigation strategies for the RBS in response to overstory removal. We used a before-after-control-impact design to study how (1) retaining residual trees or (2) eliminating soil compaction affected RBS surface counts and body condition index (BCI) up to two-years post-treatment. Additionally, we assessed how surface counts of RBS were influenced by overstory tree cover. Surface counts of RBS were not strongly influenced by overstory removal when tree residuals were retained. Body condition index increased in treatments where harvest residuals were retained. In treatments where soil compaction was eliminated, surface counts and BCI were inversely related. Finally, surface counts from both mitigation strategies were not strongly influenced by overstory cover. Overall, both mitigation techniques appeared to ameliorate impacts of overstory removal on RBS. These results highlight the importance of understanding mechanisms driving species' responses to forest management. To reduce the perceived negative effects of overstory removal on RBS, incorporating these mitigation measures may contribute to the viability and stability of RBS populations. Incorporating species' life history traits into management strategies could increase continuity of ecological function and integrity through harvesting.

RevDate: 2022-10-20

Yan Z, Yang X, Y Jin (2022)

Considerate motion imagination classification method using deep learning.

PloS one, 17(10):e0276526 pii:PONE-D-22-20363.

In order to improve the classification accuracy of motion imagination, a considerate motion imagination classification method using deep learning is proposed. Specifically, based on a graph structure suitable for electroencephalography as input, the proposed model can accurately represent the distribution of electroencephalography electrodes in non-Euclidean space and fully consider the spatial correlation between electrodes. In addition, the spatial-spectral-temporal multi-dimensional feature information was extracted from the spatial-temporal graph representation and spatial-spectral graph representation transformed from the original electroencephalography signal using the dual branch architecture. Finally, the attention mechanism and global feature aggregation module were designed and combined with graph convolution to adaptively capture the dynamic correlation intensity and effective feature of electroencephalography signals in various dimensions. A series of contrast experiments and ablation experiments on several different public brain-computer interface datasets demonstrated that the excellence of proposed method. It is worth mentioning that, the proposed model is a general framework for the classification of electroencephalography signals, which is suitable for emotion recognition, sleep staging and other fields based on electroencephalography research. Moreover, the model has the potential to be applied in the medical field of motion imagination rehabilitation in real life.

RevDate: 2022-10-20

Klinksiek M, Baco S, Leveneur S, et al (2022)

Activity-based models to predict kinetics of levulinic acid esterification.

Chemphyschem : a European journal of chemical physics and physical chemistry [Epub ahead of print].

The solvent is of prime importance in biomass conversion as it influences dissolution, reaction kinetics, catalyst activity and thermodynamic equilibrium of the reaction system. So far, activity-based models were developed to predict kinetics and equilibria, but the influence of the catalyst could not be predicted by thermodynamic models. Thus, in this work, the thermodynamic model ePC-SAFT advanced was used to predict the activities of the reactants and of the catalyst at various conditions (temperature, reactant concentrations, γ-valerolactone GVL cosolvent addition, catalyst concentration) for the homogeneously acid-catalyzed esterification of levulinic acid (LA) with ethanol. Different kinetic models were applied, and it was found that the catalyst influence on kinetics could be predicted correctly by simultaneously solving the dissociation equilibrium of H2SO4 catalyst along the reaction coordinate and the relating reaction kinetics to proton activity. ePC-SAFT advanced model parameters were only fitted to reaction-independent phase equilibrium data, but the key reaction properties were determined by one experimental kinetic curve for a set of temperatures, yielding the reaction enthalpy at standard state dH0=11.48 kJ/mol, activation energy EA=30.28kJ/mol and the intrinsic reaction rate constant k=0.011 s-1 at 323 K, which is independent of catalyst concentration. The new model allows predicting the effects of catalyst, solvent and reactant ratio on LA esterification.

RevDate: 2022-10-20

Feng L, Shan H, Zhang Y, et al (2022)

An Efficient Model-Compressed EEGNet Accelerator for Generalized Brain-Computer Interfaces with Near Sensor Intelligence.

IEEE transactions on biomedical circuits and systems, PP: [Epub ahead of print].

Brain-computer interfaces (BCIs) is promising in interacting with machines through electroencephalogram (EEG) signal. The compact end-to-end neural network model for generalized BCIs, EEGNet, has been implemented in hardware to get near sensor intelligence, but without enough efficiency. To utilize EEGNet in low-power wearable device for long-term use, this paper proposes an efficient EEGNet inference accelerator. Firstly, the EEGNet model is compressed by embedded channel selection, normalization merging, and product quantization. The customized accelerator based on the compressed model is then designed. The multilayer convolutions are achieved by reusing multiplying-accumulators and processing elements (PEs) to minimize area of logic circuits, and the weights and intermediate results are quantized to minimize memory sizes. The PEs are clock-gated to save power. Experimental results in FPGA on three datasets show the good generalizing ability of the proposed design across three BCI diagrams, which only consumes 3.31% area and 1.35% power compared to the one-to-one parallel design. The speedup factors of 1.4, 3.5, and 3.7 are achieved by embedded channel selection with negligible loss of accuracy (-0.80%). The presented accelerator is also synthesized in 65nm CMOS low power (LP) process and consumes 0.23M gates, 24.4ms/inference, 0.267mJ/inference, which is 87.22% more efficient than the implementation of EEGNet in a RISC-V MCU realized in 40nm CMOS LP process in terms of area, and 20.77% more efficient in terms of energy efficiency on BCIC-IV-2a dataset.

RevDate: 2022-10-20

Ishida S, Matsukawa Y, Yuba T, et al (2022)

Urodynamic risk factors of asymptomatic bacteriuria in men with non-neurogenic lower urinary tract symptoms.

World journal of urology [Epub ahead of print].

PURPOSE: To investigate the prevalence of asymptomatic bacteriuria (ASB) in middle-aged and older men with non-neurogenic lower urinary tract symptoms (LUTS) and clarify urodynamic factors related to the presence of ASB.

METHODS: We retrospectively reviewed the clinical data of men with LUTS who underwent urine culture examination, LUTS severity assessment, and urodynamic studies. The patients were allocated into two groups (the ASB + LUTS and LUTS-only) according to presence or absence of ASB. The patients' characteristics and urodynamic factors related to the development of ASB were assessed using univariate, binomial logistic regression, and receiver-operating characteristic (ROC) curve analyses.

RESULTS: Of 440 men, 93 (21.1%) had ASB. Parameters related to voiding functions, such as maximum flow rate, post-void residual urine volume, bladder voiding efficiency (BVE), and bladder contractility index (BCI), were significantly reduced in the ASB + LUTS group, while bladder outlet obstruction index was not different between the groups. Binomial logistic regression analysis showed that the presence of diabetes, lower BCI, and lower BVE were significantly associated with the presence of ASB. In addition, ROC analysis identified 55% as the optimal cutoff value of BVE for the presence of ASB, with a sensitivity of 84% and specificity of 83%.

CONCLUSIONS: ASB was found in > 20% of men with non-neurogenic LUTS and was associated with decreased bladder contractility and decreased BVE. BVE could predict presence of ASB with high sensitivity and specificity.

RevDate: 2022-10-18

Xu F, Li J, Dong G, et al (2022)

EEG decoding method based on multi-feature information fusion for spinal cord injury.

Neural networks : the official journal of the International Neural Network Society, 156:135-151 pii:S0893-6080(22)00364-1 [Epub ahead of print].

To develop an efficient brain-computer interface (BCI) system, electroencephalography (EEG) measures neuronal activities in different brain regions through electrodes. Many EEG-based motor imagery (MI) studies do not make full use of brain network topology. In this paper, a deep learning framework based on a modified graph convolution neural network (M-GCN) is proposed, in which temporal-frequency processing is performed on the data through modified S-transform (MST) to improve the decoding performance of original EEG signals in different types of MI recognition. MST can be matched with the spatial position relationship of the electrodes. This method fusions multiple features in the temporal-frequency-spatial domain to further improve the recognition performance. By detecting the brain function characteristics of each specific rhythm, EEG generated by imaginary movement can be effectively analyzed to obtain the subjects' intention. Finally, the EEG signals of patients with spinal cord injury (SCI) are used to establish a correlation matrix containing EEG channel information, the M-GCN is employed to decode relation features. The proposed M-GCN framework has better performance than other existing methods. The accuracy of classifying and identifying MI tasks through the M-GCN method can reach 87.456%. After 10-fold cross-validation, the average accuracy rate is 87.442%, which verifies the reliability and stability of the proposed algorithm. Furthermore, the method provides effective rehabilitation training for patients with SCI to partially restore motor function.

RevDate: 2022-10-18

Le Moigne V, Blouquit-Laye S, Desquesnes A, et al (2022)

Liposomal amikacin and Mycobacterium abscessus: intimate interactions inside eukaryotic cells.

The Journal of antimicrobial chemotherapy pii:6762396 [Epub ahead of print].

BACKGROUND: Mycobacterium abscessus (Mabs), a rapidly growing Mycobacterium species, is considered an MDR organism. Among the standard antimicrobial multi-drug regimens against Mabs, amikacin is considered as one of the most effective. Parenteral amikacin, as a consequence of its inability to penetrate inside the cells, is only active against extracellular mycobacteria. The use of inhaled liposomal amikacin may yield improved intracellular efficacy by targeting Mabs inside the cells, while reducing its systemic toxicity.

OBJECTIVES: To evaluate the colocalization of an amikacin liposomal inhalation suspension (ALIS) with intracellular Mabs, and then to measure its intracellular anti-Mabs activity.

METHODS: We evaluated the colocalization of ALIS with Mabs in eukaryotic cells such as macrophages (THP-1 and J774.2) or pulmonary epithelial cells (BCi-NS1.1 and MucilAir), using a fluorescent ALIS and GFP-expressing Mabs, to test whether ALIS reaches intracellular Mabs. We then evaluated the intracellular anti-Mabs activity of ALIS inside macrophages using cfu and/or luminescence.

RESULTS: Using confocal microscopy, we demonstrated fluorescent ALIS and GFP-Mabs colocalization in macrophages and epithelial cells. We also showed that ALIS was active against intracellular Mabs at a concentration of 32 to 64 mg/L, at 3 and 5 days post-infection. Finally, ALIS intracellular activity was confirmed when tested against 53 clinical Mabs isolates, showing intracellular growth reduction for nearly 80% of the isolates.

CONCLUSIONS: Our experiments demonstrate the intracellular localization and intracellular contact between Mabs and ALIS, and antibacterial activity against intracellular Mabs, showing promise for its future use for Mabs pulmonary infections.

RevDate: 2022-10-17

Jeong JH, Cho JH, Lee BH, et al (2022)

Real-Time Deep Neurolinguistic Learning Enhances Noninvasive Neural Language Decoding for Brain-Machine Interaction.

IEEE transactions on cybernetics, PP: [Epub ahead of print].

Electroencephalogram (EEG)-based brain-machine interface (BMI) has been utilized to help patients regain motor function and has recently been validated for its use in healthy people because of its ability to directly decipher human intentions. In particular, neurolinguistic research using EEGs has been investigated as an intuitive and naturalistic communication tool between humans and machines. In this study, the human mind directly decoded the neural languages based on speech imagery using the proposed deep neurolinguistic learning. Through real-time experiments, we evaluated whether BMI-based cooperative tasks between multiple users could be accomplished using a variety of neural languages. We successfully demonstrated a BMI system that allows a variety of scenarios, such as essential activity, collaborative play, and emotional interaction. This outcome presents a novel BMI frontier that can interact at the level of human-like intelligence in real time and extends the boundaries of the communication paradigm.

RevDate: 2022-10-17

Elliott C, Sutherland D, Gerhard D, et al (2022)

An Evaluation of the P300 Brain-Computer Interface, EyeLink Board, and Eye-Tracking Camera as Augmentative and Alternative Communication Devices.

Journal of speech, language, and hearing research : JSLHR [Epub ahead of print].

PURPOSE: Augmentative and alternative communication (AAC) systems are important to support communication for individuals with complex communication needs. A recent addition to AAC system options is the brain-computer interface (BCI). This study aimed to compare the clinical application of the P300 speller BCI with two more common AAC systems, the EyeLink board, and an eye-tracking camera.

METHOD: Ten participants without communication impairment (18-35 years of age) used each of the three AAC systems to spell three-letter words in one session. Accuracy and speed of letter selection were measured, and questionnaires were administered to evaluate usability, cognitive workload, and user preferences.

RESULTS: The results showed that the BCI was significantly less accurate, slower, and with lower usability and higher cognitive workload compared to the eye-tracking camera and EyeLink board. Participants rated the eye-tracking camera as the most favorable AAC system on all measures.

CONCLUSIONS: The results demonstrated that while the P300 speller BCI was usable by most participants, it did not function as well as the eye-tracking camera and EyeLink board. The clinical use of the BCI is, therefore, currently difficult to justify for most individuals, particularly when considering the substantial cost and setup resourcing needed.

SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.21291384.

RevDate: 2022-10-17

Klein F, Lührs M, Benitez-Andonegui A, et al (2023)

Performance comparison of systemic activity correction in functional near-infrared spectroscopy for methods with and without short distance channels.

Neurophotonics, 10(1):013503.

Significance: Functional near-infrared spectroscopy (fNIRS) is a promising tool for neurofeedback (NFB) or brain-computer interfaces (BCIs). However, fNIRS signals are typically highly contaminated by systemic activity (SA) artifacts, and, if not properly corrected, NFB or BCIs run the risk of being based on noise instead of brain activity. This risk can likely be reduced by correcting for SA, in particular when short-distance channels (SDCs) are available. Literature comparing correction methods with and without SDCs is still sparse, specifically comparisons considering single trials are lacking. Aim: This study aimed at comparing the performance of SA correction methods with and without SDCs. Approach: Semisimulated and real motor task data of healthy older adults were used. Correction methods without SDCs included a simple and a more advanced spatial filter. Correction methods with SDCs included a regression approach considering only the closest SDC and two GLM-based methods, one including all eight SDCs and one using only two a priori selected SDCs as regressors. All methods were compared with data uncorrected for SA and correction performance was assessed with quality measures quantifying signal improvement and spatial specificity at single trial level. Results: All correction methods were found to improve signal quality and enhance spatial specificity as compared with the uncorrected data. Methods with SDCs usually outperformed methods without SDCs. Correction methods without SDCs tended to overcorrect the data. However, the exact pattern of results and the degree of differences observable between correction methods varied between semisimulated and real data, and also between quality measures. Conclusions: Overall, results confirmed that both Δ [ HbO ] and Δ [ HbR ] are affected by SA and that correction methods with SDCs outperform methods without SDCs. Nonetheless, improvements in signal quality can also be achieved without SDCs and should therefore be given priority over not correcting for SA.

RevDate: 2022-10-17

Chen G, Zhang X, Zhang J, et al (2022)

A novel brain-computer interface based on audio-assisted visual evoked EEG and spatial-temporal attention CNN.

Frontiers in neurorobotics, 16:995552.

Objective: Brain-computer interface (BCI) can translate intentions directly into instructions and greatly improve the interaction experience for disabled people or some specific interactive applications. To improve the efficiency of BCI, the objective of this study is to explore the feasibility of an audio-assisted visual BCI speller and a deep learning-based single-trial event related potentials (ERP) decoding strategy.

Approach: In this study, a two-stage BCI speller combining the motion-onset visual evoked potential (mVEP) and semantically congruent audio evoked ERP was designed to output the target characters. In the first stage, the different group of characters were presented in the different locations of visual field simultaneously and the stimuli were coded to the mVEP based on a new space division multiple access scheme. And then, the target character can be output based on the audio-assisted mVEP in the second stage. Meanwhile, a spatial-temporal attention-based convolutional neural network (STA-CNN) was proposed to recognize the single-trial ERP components. The CNN can learn 2-dimentional features including the spatial information of different activated channels and time dependence among ERP components. In addition, the STA mechanism can enhance the discriminative event-related features by adaptively learning probability weights.

Main results: The performance of the proposed two-stage audio-assisted visual BCI paradigm and STA-CNN model was evaluated using the Electroencephalogram (EEG) recorded from 10 subjects. The average classification accuracy of proposed STA-CNN can reach 59.6 and 77.7% for the first and second stages, which were always significantly higher than those of the comparison methods (p < 0.05).

Significance: The proposed two-stage audio-assisted visual paradigm showed a great potential to be used to BCI speller. Moreover, through the analysis of the attention weights from time sequence and spatial topographies, it was proved that STA-CNN could effectively extract interpretable spatiotemporal EEG features.

RevDate: 2022-10-17

Zhang J, Wang T, Zhang Y, et al (2022)

Soft integration of a neural cells network and bionic interfaces.

Frontiers in bioengineering and biotechnology, 10:950235 pii:950235.

Both glial cells and neurons can be considered basic computational units in neural networks, and the brain-computer interface (BCI) can play a role in awakening the latency portion and being sensitive to positive feedback through learning. However, high-quality information gained from BCI requires invasive approaches such as microelectrodes implanted under the endocranium. As a hard foreign object in the aqueous microenvironment, the soft cerebral cortex's chronic inflammation state and scar tissue appear subsequently. To avoid the obvious defects caused by hard electrodes, this review focuses on the bioinspired neural interface, guiding and optimizing the implant system for better biocompatibility and accuracy. At the same time, the bionic techniques of signal reception and transmission interfaces are summarized and the structural units with functions similar to nerve cells are introduced. Multiple electrical and electromagnetic transmissions, regulating the secretion of neuromodulators or neurotransmitters via nanofluidic channels, have been flexibly applied. The accurate regulation of neural networks from the nanoscale to the cellular reconstruction of protein pathways will make BCI the extension of the brain.

RevDate: 2022-10-17

Xu H, Piao L, Wu Y, et al (2022)

IFN-γ enhances the antitumor activity of attenuated salmonella-mediated cancer immunotherapy by increasing M1 macrophage and CD4 and CD8 T cell counts and decreasing neutrophil counts.

Frontiers in bioengineering and biotechnology, 10:996055 pii:996055.

Bacteria-mediated cancer immunotherapy (BCI) inhibits tumor progression and has a synergistic antitumor effect when combined with chemotherapy. The anti- or pro-tumorigenic effects of interferon-γ (IFN-γ) are controversial; hence, we were interested in the antitumor effects of IFN-γ/BCI combination therapy. Here, we demonstrated that IFN-γ increased the tumor cell killing efficacy of attenuated Salmonella by prolonging the survival of tumor-colonizing bacteria via blockade of tumor-infiltrating neutrophil recruitment. In addition, IFN-γ attenuated Salmonella-stimulated immune responses by stimulating tumor infiltration by M1-like macrophages and CD4+ and CD8+ T cells, thereby facilitating tumor eradication. Taken together, these findings suggest that combination treatment with IFN-γ boosts the therapeutic response of BCI with S. tΔppGpp, suggesting that IFN-γ/BCI is a promising approach to immunotherapy.

RevDate: 2022-10-17

Collinger JL, DJ Krusienski (2022)

The 8th international brain-computer interface meeting, BCIs: the next frontier.

Brain computer interfaces (Abingdon, England), 9(2):67-68.

RevDate: 2022-10-17

Sun M (2022)

Study on Antidepressant Emotion Regulation Based on Feedback Analysis of Music Therapy with Brain-Computer Interface.

Computational and mathematical methods in medicine, 2022:7200678.

In today's society, people with poor mental ability are prone to neuropsychiatric diseases such as anxiety, ADHD, and depression due to long-term negative emotions. Although conventional Western medicine has certain curative effect, these drugs have significant anticholinergic side effects central toxicity as well as cardiovascular and gastrointestinal side effects which limit their application in the elderly. At present, several antidepressants used in clinic have certain limitations. According to the symptoms of depression, this paper proposes a feedback emotion regulation method of brain-computer interface music therapy. This method uses special music stimulation to regulate the release of inhibiting sex hormones in the body, reduce the influence of negative emotions on the internal environment of the body, and maintain the steady state of the body. In this method, EEG is used as the emotional control signal of depressed patients, and this biological signal is transformed into music that depressed patients can understand, so as to clarify their physiological and psychological state and realize emotional self-regulation by feedback.

RevDate: 2022-10-14

Chen J, Meng X, Liu Z, et al (2022)

Decoding semantics from intermodulation responses in frequency-tagged stereotactic EEG.

Journal of neuroscience methods pii:S0165-0270(22)00253-9 [Epub ahead of print].

BACKGROUND: Humans perform object recognition using holistic processing, which is different from computers. Intermodulation responses in the steady-state visual evoked potential (SSVEP) of scalp electroencephalography (EEG) had recently been used as an objective label for holistic processing.

NEW METHOD: Using stereotactic EEG (sEEG) to record SSVEP directly from inside of the brain, we decoded Chinese characters from non-characters with activation from multiple brain areas including occipital, parietal, temporal, and frontal cortices.

RESULTS: Semantic categories could be decoded from responses at the intermodulation frequency with high accuracy (80%-90%), but not the base frequency. Moreover, semantic categories could be decoded with activation from multiple areas including temporal, parietal, and frontal areas.

Previous studies investigated holistic processing in faces and words with frequency-tagged scalp EEGs. The current study extended the results to stereotactic EEG signals directly recorded from the brain.

CONCLUSIONS: The human brain applies holistic processing in recognizing objects like Chinese characters. Our findings could be extended to an add-on feature in the existing SSVEP BCI speller.

RevDate: 2022-10-14

Wang W, Li B, H Wang (2022)

A novel end-to-end BG-Attention algorithm for denoising of EEG signals.

Neuroscience pii:S0306-4522(22)00507-3 [Epub ahead of print].

Electroencephalography (EEG) signals are nonlinear and non-stationary sequences that carry much information. However, physiological signals from other body regions may readily interfere with EEG signal capture, having a significant unfavorable influence on subsequent analysis. Therefore, signal denoising is a crucial step in EEG signal processing. This paper proposes a bidirectional gated recurrent unit (GRU) network based on a self-attention mechanism (BG-Attention) for extracting pure EEG signals from noise-contaminated EEG signals. The bidirectional GRU network can simultaneously capture past and future information while processing continuous time sequence. And by paying different levels of attention to the content of varying importance, the model can learn more significant feature of EEG signal sequences, highlighting the contribution of essential samples to denoising. The proposed model is evaluated on the EEGdenoiseNet data set. We compared the proposed model with a fully connected network (FCNN), the one-dimensional residual convolutional neural network (1D-ResCNN), and a recurrent neural network (RNN). The experimental results show that the proposed model can reconstruct a clear EEG waveform with a decent signal-to-noise ratio (SNR) and the relative root mean squared error (RRMSE) value. This study demonstrates the potential of BG-Attention in the pre-processing phase of EEG experiments, which has significant implications for medical technology and brain-computer interface (BCI) applications.

RevDate: 2022-10-14

Faes A, MM Van Hulle (2022)

Finger movement and coactivation predicted from intracranial brain activity using extended Block-Term Tensor Regression.

Journal of neural engineering [Epub ahead of print].

Objective We introduce extended Block-Term Tensor Regression (eBTTR), a novel regression method designed to account for the multilinear nature of human intracranial finger movement recordings. Approach The proposed method relies on recursive Tucker decomposition combined with automatic component extraction. Main results eBTTR outperforms state-of-the-art regression approaches, including multilinear and deep learning ones, in accurately predicting finger trajectories as well as unintentional finger coactivations. Significance eBTTR rivals state-of-the-art approaches while being less computationally expensive which is an advantage when intracranial electrodes are implanted acutely, as part of the patient's presurgical workup, limiting time for decoder development and testing.

RevDate: 2022-10-14

Zhang L, Ren H, Zhang R, et al (2022)

Time-estimation process could cause the disappearence of readiness potential.

Cognitive neurodynamics, 16(5):1003-1011.

Generally, the readiness potential (RP) is considered to be the scalp electroencephalography (EEG) activity preceding movement. In our previous study, we found early RP was absent among approximately half of the subjects during instructed action, but we did not identify the mechanism causing the disappearance of the RP. In this study, we investigated whether the time-estimation process could cause the disappearance of the RP. First, we designed experiments consisting of motor execution (ME), motor execution after time estimation (MEATE), and time estimation (TE) tasks, and we collected and preprocessed the EEG data of 16 subjects. Second, we compared the event related potential (ERP) waveform and scalp topography between ME and MEATE tasks. Then, to explore the influence of time-estimation, we analyzed the difference in ERP between MEATE and TE tasks. Finally, we used source imaging to probe the activation of brain regions during the three tasks, and we calculated the average activation amplitude of eight motor related brain regions. We found that the RP occurred in the ME task but not in the MEATE task. We also found that the waveform of the difference in ERP between the MEATE and TE tasks was similar to that of the ME task. The results of source imaging indicated that, compared to the ME task, the activation amplitude of the supplementary motor area (SMA) decreased significantly for the MEATE task. Our results suggested that the time estimation process could cause the disappearance of the RP. This phenomenon might be caused by the counteraction of neural electrical activity related to time estimation and motor preparation in the SMA.

RevDate: 2022-10-14

Fu R, Xu D, Li W, et al (2022)

Single-trial motor imagery electroencephalogram intention recognition by optimal discriminant hyperplane and interpretable discriminative rectangle mixture model.

Cognitive neurodynamics, 16(5):1073-1085.

Spatial filtering is widely used in brain-computer interface (BCI) systems to augmented signal characteristics of electroencephalogram (EEG) signals. In this study, a spatial domain filtering based EEG feature extraction method, optimal discriminant hyperplane-common spatial subspace decomposition (ODH-CSSD) is proposed. Specifically, the multi-dimensional EEG features were extracted from the original EEG signals by common space subspace decomposition (CSSD) algorithm, and the optimal feature criterion was established to find the multi-dimensional optimal projection space. A classic method of data dimension optimizing is using the eigenvectors of a lumped covariance matrix corresponding to the maximum eigenvalues. Then, the cost function is defined as the extreme value of the discriminant criterion, and the orthogonal N discriminant vectors corresponding to the N extreme value of the criterion are solved and constructed into the N-dimensional optimal feature space. Finally, the multi-dimensional EEG features are projected into the N-dimensional optimal projection space to obtain the optimal N-dimensional EEG features. Moreover, this study involves the extraction of two-dimensional and three-dimensional optimal EEG features from motor imagery EEG datasets, and the optimal EEG features are identified using the interpretable discriminative rectangular mixture model (DRMM). Experimental results show that the accuracy of DRMM to identify two-dimensional optimal features is more than 0.91, and the highest accuracy even reaches 0.975. Meanwhile, DRMM has the most stable recognition accuracy for two-dimensional optimal features, and its average clustering accuracy reaches 0.942, the gap between the accuracy of the DRMM with the accuracy of the FCM and K-means can reach 0.26. And the optimal three-dimensional features, for most subjects, the clustering accuracy of DRMM is higher than that of FCM and K-means. In general, the decision rectangle obtained by DRMM can clearly explain the difference of each cluster, notably, the optimization of multidimensional EEG features by optimal projection is superior to Fisher's ratio, and this method provides an alternative for the application of BCI.

Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-021-09768-w.

RevDate: 2022-10-14

Dong E, Zhang H, Zhu L, et al (2022)

A multi-modal brain-computer interface based on threshold discrimination and its application in wheelchair control.

Cognitive neurodynamics, 16(5):1123-1133.

In this study, we propose a novel multi-modal brain-computer interface (BCI) system based on the threshold discrimination, which is proposed for the first time to distinguish between SSVEP and MI potentials. The system combines these two heterogeneous signals to increase the number of control commands and improve the performance of asynchronous control of external devices. In this research, an electric wheelchair is controlled as an example. The user can continuously control the wheelchair to turn left/right through motion imagination (MI) by imagining left/right-hand movement and generate another 6 commands for the wheelchair control by focusing on the SSVEP stimulation panel. Ten subjects participated in a MI training session and eight of them completed a mobile obstacle-avoidance experiment in a complex environment requesting high control accuracy for successful manipulation. Comparing with the single-modal BCI-controlled wheelchair system, the results demonstrate that the proposed multi-modal method is effective by providing more satisfactory control accuracy, and show the potential of BCI-controlled systems to be applied in complex daily tasks.

RevDate: 2022-10-14

Bagherzadeh S, Maghooli K, Shalbaf A, et al (2022)

Emotion recognition using effective connectivity and pre-trained convolutional neural networks in EEG signals.

Cognitive neurodynamics, 16(5):1087-1106.

Convolutional Neural Networks (CNN) have recently made considerable advances in the field of biomedical signal processing. These methodologies can assist in emotion recognition for affective brain computer interface. In this paper, a novel emotion recognition system based on the effective connectivity and the fine-tuned CNNs from multichannel Electroencephalogram (EEG) signal is presented. After preprocessing EEG signals, the relationships among 32 channels of EEG in the form of effective brain connectivity analysis which represents information flow between regions are computed by direct Directed Transfer Function (dDTF) method which yields a 32*32 image. Then, these constructed images from EEG signals for each subject were fed as input to four versions of pre-trained CNN models, AlexNet, ResNet-50, Inception-v3 and VGG-19 and the parameters of these models are fine-tuned, independently. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals in frequency bands. The efficiency of the proposed approach is evaluated on MAHNOB-HCI and DEAP databases. The experiments for classifying five emotional states show that the ResNet-50 applied on dDTF images in alpha band achieves best results due to specific architecture which captures the brain connectivity, efficiently. The accuracy and F1-score values for MAHNOB-HCI were obtained 99.41, 99.42 and for DEAP databases, 98.17, and 98.23. Newly proposed model is capable of effectively analyzing the brain function using information flow from multichannel EEG signals using effective connectivity measure of dDTF and ResNet-50.

RevDate: 2022-10-14

Tao Q, Jiang L, Li F, et al (2022)

Dynamic networks of P300-related process.

Cognitive neurodynamics, 16(5):975-985.

P300 as an effective biomarker to index attention and memory has been widely used for brain-computer interface, cognitive evaluation, and clinical diagnosis. To evoke clear P300, an oddball paradigm consisting of two types of stimuli, i.e., infrequent target stimuli and frequent standard stimuli, is usually used. However, to simply and quickly explore the P300-related process, previous studies predominately focused on the target condition but ignored the fusion of target and standard conditions, as well as the difference of brain networks between them. Therefore, in this study, we used the hidden Markov model to investigate the fused multi-conditional electroencephalogram dataset of P300, aiming to effectively identify the underlying brain networks and explore the difference between conditions. Specifically, the inferred networks, including their transition sequences and spatial distributions, were scrutinized first. Then, we found that the difference between target and standard conditions was mainly concentrated in two phases. One was the stimulation phase that mainly related to the cortical activities of the postcentral gyrus and superior parietal lobule, and the other corresponded to the response phase that involved the activities of superior and medial frontal gyri. This might be attributed to distinct cognitive functions, as the stimulation phase is associated with visual information integration whereas the response phase involves stimulus discrimination and behavior control. Taken together, the current work explored dynamic networks underlying the P300-related process and provided a complementary understanding of distinct P300 conditions, which may contribute to the design of P300-related brain-machine systems.

RevDate: 2022-10-14

Antony MJ, Sankaralingam BP, Mahendran RK, et al (2022)

Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis.

Sensors (Basel, Switzerland), 22(19): pii:s22197596.

An efficient feature extraction method for two classes of electroencephalography (EEG) is demonstrated using Common Spatial Patterns (CSP) with optimal spatial filters. However, the effects of artifacts and non-stationary uncertainty are more pronounced when CSP filtering is used. Furthermore, traditional CSP methods lack frequency domain information and require many input channels. Therefore, to overcome this shortcoming, a feature extraction method based on Online Recursive Independent Component Analysis (ORICA)-CSP is proposed. For EEG-based brain-computer interfaces (BCIs), especially online and real-time BCIs, the most widely used classifiers used to be linear discriminant analysis (LDA) and support vector machines (SVM). Previous evaluations clearly show that SVMs generally outperform other classifiers in terms of performance. In this case, Adaptive Support Vector Machine (A-SVM) is used for classification together with the ORICA-CSP method. The results are promising, and the experiments are performed on EEG data of 4 classes' motor images, namely Dataset 2a of BCI Competition IV.

RevDate: 2022-10-14

Sharifi S, Maleki Dizaj S, Ahmadian E, et al (2022)

A Biodegradable Flexible Micro/Nano-Structured Porous Hemostatic Dental Sponge.

Nanomaterials (Basel, Switzerland), 12(19): pii:nano12193436.

A biodegradable micro/nano-structured porous hemostatic gelatin-based sponge as a dentistry surgery foam was prepared using a freeze-drying method. In vitro function evaluation tests were performed to ensure its hemostatic effect. Biocompatibility tests were also performed to show the compatibility of the sponge on human fetal foreskin fibroblasts (HFFF2) cells and red blood cells (RBCs). Then, 10 patients who required the extraction of two teeth were selected, and after teeth extraction, for dressing, the produced sponge was placed in one of the extracavities while a commercial sponge was placed in the cavity in the other tooth as a control. The total weight of the absorbed blood in each group was compared. The results showed a porous structure with micrometric and nanometric pores, flexibility, a two-week range for degradation, and an ability to absorb blood 35 times its weight in vitro. The prepared sponge showed lower blood clotting times (BCTs) (243.33 ± 2.35 s) and a lower blood clotting index (BCI) (10.67 ± 0.004%) compared to two commercial sponges that displayed its ability for faster coagulation and good hemostatic function. It also had no toxic effects on the HFFF2 cells and RBCs. The clinical assessment showed a better ability of blood absorption for the produced sponge (p-value = 0.0015). The sponge is recommended for use in dental surgeries because of its outstanding abilities.

RevDate: 2022-10-14

Miljević M, Čabrilo B, Budinski I, et al (2022)

Host-Parasite Relationship-Nematode Communities in Populations of Small Mammals.

Animals : an open access journal from MDPI, 12(19): pii:ani12192617.

Nematode burdens and variation in morphological characteristics were assessed in eighty-eight animals from three host species (Apodemus sylvaticus, Apodemus flavicollis, and Myodes glareolus) from eight localities in Serbia. In total, 15 species of nematodes were identified, and the overall mean parasite species richness (IndPSR) was 1.61 per animal (1.98 in A. flavicollis, 1.43 in M. glareolus, and 0.83 in A. sylvaticus). Furthermore, the studied host species significantly differed in individual parasite load (IndPL) and in the following morphological characters: spleen mass, body condition index (BCI), and body mass. We aimed to analyze the relationship between the burden of intestinal nematodes, on one hand, and the body conditions of the host and its capability to develop immune defends on the other. Spleen mass was considered as a measure of immune response. In all host species, larger animals with a better condition (higher BCI) were infected with more parasites species (IndPSR), while parasite load was not related to BCI. Only in A. flavicollis were males significantly larger, but females of the same sizes were infected with more parasite species. This female-biased parasitism is contrary to the theoretical expectation that males should be more parasitized, being larger, more active, with a wider home range. Although the spleen size was significantly correlated with body condition and body mass, IndPSR was not related to spleen mass in any studied species, but in M. galareolus, we found that a smaller spleen was related to higher infection intensity (IndPL).

RevDate: 2022-10-13

Brickwedde M, Bezsudnova Y, Kowalczyk A, et al (2022)

Application of rapid invisible frequency tagging for brain computer interfaces.

Journal of neuroscience methods pii:S0165-0270(22)00252-7 [Epub ahead of print].

BACKGROUND: Brain-computer interfaces (BCI) based on steady-state visual evoked potentials (SSVEPs/SSVEFs) are among the most commonly used BCI systems. They require participants to covertly attend to visual objects flickering at specified frequencies. The attended location is decoded online by analysing the power of neuronal responses at the flicker frequency.

NEW METHOD: We implemented a novel rapid invisible frequency-tagging technique, utilizing a state-of-the-art projector with refresh rates of up to 1440Hz. We flickered the luminance of visual objects at 56 and 60Hz, which was invisible to participants but produced strong neuronal responses measurable with magnetoencephalography (MEG). The direction of covert attention, decoded from frequency-tagging responses, was used to control an online BCI PONG game.

RESULTS: Our results show that seven out of eight participants were able to play the pong game controlled by the frequency-tagging signal, with average accuracies exceeding 60%. Importantly, participants were able to modulate the power of the frequency-tagging response within a 1-second interval, while only seven occipital sensors were required to reliably decode the neuronal response.

In contrast to existing SSVEP-based BCI systems, rapid frequency-tagging does not produce a visible flicker. This extends the time-period participants can use it without fatigue, by avoiding distracting visual input. Furthermore, higher frequencies increase the temporal resolution of decoding, resulting in higher communication rates.

CONCLUSION: Using rapid invisible frequency-tagging opens new avenues for fundamental research and practical applications. In combination with novel optically pumped magnetometers (OPMs), it could facilitate the development of high-speed and mobile next-generation BCI systems.

RevDate: 2022-10-13

Tong J, Wei X, Dong E, et al (2022)

Hybrid mental tasks based human computer interface via integration of pronunciation and motor imagery.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Among the existing active brain-computer interfaces (BCI), the motor imagination (MI) is widely used. To operate the MI BCI effectively, subjects need to carry out trainings on corresponding imagining tasks. Here, we studied how to reduce the discomfort and fatigue of active BCI imaginary tasks and the inability to concentrate on them while improving the accuracy.

APPROACH: This paper proposes a hybrid BCI composed of MI and pronunciation imagination (PI). The EEG signals of 10 subjects are recognized by the adaptive Riemannian distance classification and the improved frequency selective filter-bank Common Spatial Pattern recognition.

MAIN RESULTS: The results show that under the new paradigm with the combination of MI and PI, the recognition accuracy is higher than the MI alone. The highest recognition rate of the proposed hybrid system can reach more than 90%. Furthermore, through the subjects' scoring results of the operation difficulty, it is concluded that the designed hybrid paradigm is more operable than the traditional BCI paradigm.

SIGNIFICANCE: The separable tasks in the active BCI are limited and the accuracy needs to be improved. The new hybrid paradigm proposed by us improves the accuracy and operability of the active BCI system, providing a new possibility for the research direction of the active BCI.

RevDate: 2022-10-13
CmpDate: 2022-10-13

Chengaiyan S, K Anandan (2022)

Effect of functional and effective brain connectivity in identifying vowels from articulation imagery procedures.

Cognitive processing, 23(4):593-618.

Articulation imagery, a form of mental imagery, refers to the activity of imagining or speaking to oneself mentally without an articulation movement. It is an effective domain of research in speech impaired neural disorders, as speech imagination has high similarity to real voice communication. This work employs electroencephalography (EEG) signals acquired from articulation and articulation imagery in identifying the vowel being imagined during different tasks. EEG signals from chosen electrodes are decomposed using the empirical mode decomposition (EMD) method into a series of intrinsic mode functions. Brain connectivity estimators and entropy measures have been computed to analyze the functional cooperation and causal dependence between different cortical regions as well as the regularity in the signals. Using machine learning techniques such as multiclass support vector machine (MSVM) and random forest (RF), the vowels have been classified. Three different training and testing protocols (Articulation-AR, Articulation imagery-AI and Articulation vs Articulation imagery-AR vs AI) were employed for identifying the vowel being imagined of articulating. An overall classification accuracy of 80% was obtained for articulation imagery protocol which was found to be higher than the other two protocols. Also, MSVM techniques outperformed the RF technique in terms of the classification accuracy. The effect of brain connectivity estimators and machine learning techniques seems to be reliable in identifying the vowel from the subjects' thought and thereby assisting the people with speech impairment.

RevDate: 2022-10-12

Huang WC, Hung CH, Lin YW, et al (2022)

Electrically Copolymerized Polydopamine Melanin/Poly(3,4-ethylenedioxythiophene) Applied for Bioactive Multimodal Neural Interfaces with Induced Pluripotent Stem Cell-Derived Neurons.

ACS biomaterials science & engineering [Epub ahead of print].

Multimodal neural interfaces include combined functions of electrical neuromodulation and synchronic monitoring of neurochemical and physiological signals in one device. The remarkable biocompatibility and electrochemical performance of polystyrene sulfonate-doped poly(3,4-ethylenedioxythiophene) (PEDOT:PSS) have made it the most recommended conductive polymer neural electrode material. However, PEDOT:PSS formed by electrochemical deposition, called PEDOT/PSS, often need multiple doping to improve structural instability in moisture, resolve the difficulties of functionalization, and overcome the poor cellular affinity. In this work, inspired by the catechol-derived adhesion and semiconductive properties of polydopamine melanin (PDAM), we used electrochemical oxidation polymerization to develop PDAM-doped PEDOT (PEDOT/PDAM) as a bioactive multimodal neural interface that permits robust electrochemical performance, structural stability, analyte-trapping capacity, and neural stem cell affinity. The use of potentiodynamic scans resolved the problem of copolymerizing 3,4-ethylenedioxythiophene (EDOT) and dopamine (DA), enabling the formation of PEDOT/PDAM self-assembled nanodomains with an ideal doping state associated with remarkable current storage and charge transfer capacity. Owing to the richness of hydrogen bond donors/acceptors provided by the hydroxyl groups of PDAM, PEDOT/PDAM presented better electrochemical and mechanical stability than PEDOT/PSS. It has also enabled high sensitivity and selectivity in the electrochemical detection of DA. Different from PEDOT/PSS, which inhibited the survival of human induced pluripotent stem cell-derived neural progenitor cells, PEDOT/PDAM maintained cell proliferation and even promoted cell differentiation into neuronal networks. Finally, PEDOT/PDAM was modified on a commercialized microelectrode array system, which resulted in the reduction of impedance by more than one order of magnitude; this significantly improved the resolution and reduced the noise of neuronal signal recording. With these advantages, PEDOT/PDAM is anticipated to be an efficient bioactive multimodal neural electrode material with potential application to brain-machine interfaces.

RevDate: 2022-10-12

Zhong D, Zhan Z, Zhang J, et al (2022)

SMYD3 regulates the abnormal proliferation of non-small-cell lung cancer cells via the H3K4me3/ANO1 axis.

Journal of biosciences, 47:.

Non-small-cell lung cancer (NSCLC) is the most prevalent type of lung cancer. This study evaluated the mechanism of histone methyltransferase SET and MYND domain-containing 3 (SMYD3) in the abnormal proliferation of NSCLC cells. The human bronchial epithelial cell (HBEC) line (16HBE) and NSCLC cell lines (H1299, A549, H460, and H1650) were collected. A549 and H1650 cells were transfected with si-SMYD3 and Anoctamin-1 (ANO1) and their negative controls or treated with BCI-121, or A549 cells were treated with CPI-455. SMYD3, H3 lysine 4 tri-methylation (H3K4me3), and ANO1 levels in the cells were detected. The proliferation ability of A549 and H1650 cells were examined. We found that SMYD3, H3K4me3, and ANO1 were highly expressed in NSCLC cell lines. Silencing SMYD3 or SMYD3 activity in A549 and H1650 cells inhibited the cell proliferation ability and decreased H3K4me3 level and ANO1 mRNA level in the cells. H3K4me3 upregulation orANO1 overexpression reversed the inhibitory effects of silencing SMYD3 on the abnormal proliferation of NSCLC cells. Chromatin-Immunoprecipitation (Ch-IP) assay detected that SMYD3 bound to and enriched in the ANO1 promoter region, and the ANO1 promoter region was enriched with H3K4me3. Collectively, SMYD3 promoted ANO1 transcription by upregulating H3K4me3 in the ANO1 promoter region, thus facilitating the abnormal proliferation of NSCLC cells.

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Researcher

Robbins holds BS, MS, and PhD degrees in the life sciences. He served as a tenured faculty member in the Zoology and Biological Science departments at Michigan State University. He is currently exploring the intersection between genomics, microbial ecology, and biodiversity — an area that promises to transform our understanding of the biosphere.

Educator

Robbins has extensive experience in college-level education: At MSU he taught introductory biology, genetics, and population genetics. At JHU, he was an instructor for a special course on biological database design. At FHCRC, he team-taught a graduate-level course on the history of genetics. At Bellevue College he taught medical informatics.

Administrator

Robbins has been involved in science administration at both the federal and the institutional levels. At NSF he was a program officer for database activities in the life sciences, at DOE he was a program officer for information infrastructure in the human genome project. At the Fred Hutchinson Cancer Research Center, he served as a vice president for fifteen years.

Technologist

Robbins has been involved with information technology since writing his first Fortran program as a college student. At NSF he was the first program officer for database activities in the life sciences. At JHU he held an appointment in the CS department and served as director of the informatics core for the Genome Data Base. At the FHCRC he was VP for Information Technology.

Publisher

While still at Michigan State, Robbins started his first publishing venture, founding a small company that addressed the short-run publishing needs of instructors in very large undergraduate classes. For more than 20 years, Robbins has been operating The Electronic Scholarly Publishing Project, a web site dedicated to the digital publishing of critical works in science, especially classical genetics.

Speaker

Robbins is well-known for his speaking abilities and is often called upon to provide keynote or plenary addresses at international meetings. For example, in July, 2012, he gave a well-received keynote address at the Global Biodiversity Informatics Congress, sponsored by GBIF and held in Copenhagen. The slides from that talk can be seen HERE.

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Robbins is a skilled meeting facilitator. He prefers a participatory approach, with part of the meeting involving dynamic breakout groups, created by the participants in real time: (1) individuals propose breakout groups; (2) everyone signs up for one (or more) groups; (3) the groups with the most interested parties then meet, with reports from each group presented and discussed in a subsequent plenary session.

Designer

Robbins has been engaged with photography and design since the 1960s, when he worked for a professional photography laboratory. He now prefers digital photography and tools for their precision and reproducibility. He designed his first web site more than 20 years ago and he personally designed and implemented this web site. He engages in graphic design as a hobby.

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Rajesh Rao has written the perfect introduction to the exciting world of brain-computer interfaces. The book is remarkably comprehensive — not only including full descriptions of classic and current experiments but also covering essential background concepts, from the brain to Bayes and back. Brain-Computer Interfacing will be welcomed by a wide range of intelligent readers interested in understanding the first steps toward the symbiotic merger of brains and computers. Eberhard E. Fetz, UW

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Reprints and preprints of publications, slide presentations, instructional materials, and data compilations written or prepared by Robert Robbins. Most papers deal with computational biology, genome informatics, using information technology to support biomedical research, and related matters.

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