المؤلفون: Dheeraj Rathee, Haider Raza, Sujit Roy, Girijesh Prasad

المصدر: Scientific Data, Vol 8, Iss 1, Pp 1-10 (2021)
Rathee, D, Raza, H, Roy, S & Prasad, G 2021, ' A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface ', Scientific Data, vol. 8, no. 1, 120 . https://doi.org/10.1038/s41597-021-00899-7Test
Scientific Data

مصطلحات موضوعية: Adult, Male, Statistics and Probability, Data Descriptor, Computer science, Speech recognition, Science, Neuroimaging, Brain imaging, 02 engineering and technology, Motor Activity, Library and Information Sciences, Pattern Recognition, Automated, Education, Machine Learning, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Motor imagery, Cognition, 0202 electrical engineering, electronic engineering, information engineering, medicine, Humans, Brain–computer interface, medicine.diagnostic_test, Subtraction, Magnetoencephalography, Computer Science Applications, Brain-Computer Interfaces, Pattern recognition (psychology), 020201 artificial intelligence & image processing, Female, Statistics, Probability and Uncertainty, Biomedical engineering, 030217 neurology & neurosurgery, Mental image, Information Systems

الوصف: Recent advancements in magnetoencephalography (MEG)-based brain-computer interfaces (BCIs) have shown great potential. However, the performance of current MEG-BCI systems is still inadequate and one of the main reasons for this is the unavailability of open-source MEG-BCI datasets. MEG systems are expensive and hence MEG datasets are not readily available for researchers to develop effective and efficient BCI-related signal processing algorithms. In this work, we release a 306-channel MEG-BCI data recorded at 1KHz sampling frequency during four mental imagery tasks (i.e. hand imagery, feet imagery, subtraction imagery, and word generation imagery). The dataset contains two sessions of MEG recordings performed on separate days from 17 healthy participants using a typical BCI imagery paradigm. The current dataset will be the only publicly available MEG imagery BCI dataset as per our knowledge. The dataset can be used by the scientific community towards the development of novel pattern recognition machine learning methods to detect brain activities related to motor imagery and cognitive imagery tasks using MEG signals.
Measurement(s) brain physiology trait Technology Type(s) Magnetoencephalography Factor Type(s) age group • sex Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13561976

وصف الملف: application/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b642826a8c1af02867e74097976ddacTest
https://doaj.org/article/275f143ab0d1404595e3d033152d8f9cTest

المؤلفون: Yu Qi, Yu Sun, Yi Sun, Iinze Qian, Cuntai Guan, Zhao Feng, Yueming Wang

المساهمون: School of Computer Science and Engineering

المصدر: IEEE transactions on bio-medical engineering. 69(5)

مصطلحات موضوعية: medicine.diagnostic_test, Computer science, Speech recognition, Feature extraction, Biomedical Engineering, Neurological Rehabilitation, Brain Computer Interface, Bayes Theorem, Electroencephalography, Naive Bayes classifier, Motor imagery, Feature (computer vision), Brain-Computer Interfaces, medicine, Imagination, Humans, Medicine [Science], Motor Imagery, Transfer of learning, Neurorehabilitation, Algorithms, Brain–computer interface

الوصف: Brain-computer interfaces (BCI) that enables people with severe motor disabilities to use their brain signals for direct control of objects have attracted increased interest in rehabilitation. To date, no study has investigated feasibility of the BCI framework incorporating both intracortical and scalp signals. Methods: Concurrent local field potential (LFP) from the hand-knob area and scalp EEG were recorded in a paraplegic patient undergoing a spike-based close-loop neurorehabilitation training. Based upon multimodal spatio-spectral feature extraction and Naive Bayes classification, we developed, for the first time, a novel LFP-EEG-BCI for motor intention decoding. A transfer learning (TL) approach was employed to further improve the feasibility. The performance of the proposed LFP-EEG-BCI for four-class upper-limb motor intention decoding was assessed. Results: Using a decision fusion strategy, we showed that the LFP-EEG-BCI significantly (p

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f59ddbd5b4d0f43b5c74e4f8301513edTest
https://pubmed.ncbi.nlm.nih.gov/34582344Test

المؤلفون: Haseeb Ur Rehman, Wu Zhang, Rao Zain Ul Abideen, Muhammad Aqeel, Waseem Abbas, Sadaqat Ali Rammy, Haider Riaz, Syed Shahid Mahmood

المصدر: Biocybernetics and Biomedical Engineering. 41:97-110

مصطلحات موضوعية: medicine.diagnostic_test, Artificial neural network, business.industry, Computer science, Interface (computing), Deep learning, Biomedical Engineering, Pattern recognition, Electroencephalography, Set (abstract data type), Motor imagery, Robustness (computer science), medicine, Artificial intelligence, business, Brain–computer interface

الوصف: Electroencephalography (EEG) is a method of the brain–computer interface (BCI) that measures brain activities. EEG is a method of (non-)invasive recording of the electrical activity of the brain. This can be used to build BCIs. From the last decade, EEG has grasped researchers’ attention to distinguish human activities. However, temporal information has rarely been retained to incorporate temporal information for multi-class (more than two classes) motor imagery classification. This research proposes a long-short-term-memory-based deep learning model to learn the hidden sequential patterns. Two types of features are used to feed the proposed model, including Fourier Transform Energy Maps (FTEMs) and Common Spatial Patterns (CSPs) filters. Multiple experiments have been conducted on a publicly available dataset. Extraction of spatial and spectro-temporal features using CSP filters and FTEM allow the sequence-to-sequence based proposed model to learn the hidden sequential features. The proposed method is trained, evaluated, and optimized for a publicly available benchmark data set and resulted in 0.81 mean kappa value. Obtained results depict the model robustness for the artifacts and suitable for real-life applications with comparable classification accuracy. The code and findings will be available at https://github.com/waseemabbaasTest/Motor-Imagery-Classification.git .

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::a0ad413d61e404e212d256e60743f04aTest
https://doi.org/10.1016/j.bbe.2020.12.004Test

المؤلفون: Yang Zhang, Jiancai Leng, Fenqi Rong, Fangzhou Xu, Tao Sun, Siddharth Siddharth, Tzyy-Ping Jung

المصدر: IEEE Transactions on Neural Systems and Rehabilitation Engineering. 29:2417-2424

مصطلحات موضوعية: Stroke patient, Computer science, Biomedical Engineering, Electroencephalography, Convolutional neural network, Motor imagery, Internal Medicine, medicine, Humans, Spatial analysis, Supplementary data, medicine.diagnostic_test, business.industry, General Neuroscience, Deep learning, Rehabilitation, Pattern recognition, Hand, Stroke, Visual inspection, Brain-Computer Interfaces, Imagination, Artificial intelligence, business, Algorithms

الوصف: Acquiring Electroencephalography (EEG) data is often time-consuming, laborious, and costly, posing practical challenges to train powerful but data-demanding deep learning models. This study proposes a surrogate EEG data-generation system based on cycle-consistent adversarial networks (CycleGAN) that can expand the number of training data. This study used EEG2Image based on a modified S-transform (MST) to convert EEG data into EEG-topography. This method retains the frequency-domain characteristics and spatial information of the EEG signals. Then, the CycleGAN is used to learn and generate motor-imagery EEG data of stroke patients. From the visual inspection, there is no difference between the EEG topographies of the generated and original EEG data collected from the stroke patients. Finally, we used convolutional neural networks (CNN) to evaluate and analyze the generated EEG data. The experimental results show that the generated data effectively improved the classification accuracy.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::357b0037fca574467108965c18c44edfTest
https://doi.org/10.1109/tnsre.2021.3123969Test

المؤلفون: Daniel Prado Campos, Marcelo C. M. Teixeira, André Eugênio Lazzaretti, Eddy Krueger, Paulo Broniera Junior, Aparecido Augusto de Carvalho, Percy Nohama

المساهمون: Universidade Estadual Paulista (Unesp), Universidade Tecnológica Federal do Paraná (UTFPR), Universidade Estadual de Londrina (UEL), IoT e Manufatura 4.0

المصدر: Scopus
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP

مصطلحات موضوعية: medicine.medical_specialty, Computer science, 0206 medical engineering, Biomedical Engineering, Closed-loop systems, Health Informatics, 02 engineering and technology, Isometric exercise, Spinal cord injury, Electroencephalography, Setpoint, 03 medical and health sciences, 0302 clinical medicine, Physical medicine and rehabilitation, Motor imagery, Functional electrical stimulation, medicine, Brain–computer interface, medicine.diagnostic_test, medicine.disease, 020601 biomedical engineering, Neuromodulation (medicine), Signal Processing, Motor Imagery, Paraplegia, Mechanomyography, 030217 neurology & neurosurgery

الوصف: Made available in DSpace on 2021-06-25T10:29:02Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-07-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) People with spinal cord injury (SCI) may have their paralyzed muscles activated through functional electrical stimulation (FES). This neuromodulation technique has been used frequently to assist in controlling the movement of neuroprostheses. Electroencephalography (EEG) is able to trigger FES from the motor imagery captured through movements intentions. This research presents an isometric neuromuscular control system of the quadriceps muscle activated by EEG. Additionally, the detection of neuromuscular fatigue through the mechanomyography (MMG) technique is proposed, which is used to shut-off the system. A pilot study was performed on a chronic 42-year-old paraplegic (no voluntary contraction below the spinal cord injury level T8) volunteer. To do so, the training procedure for EEG signals was divided into the calibration and feedback phases. In the first one, four EEG channels and the Linear Discriminant Analysis (LDA) classifier were used to classify between motor imagery of the right leg and remain at rest. The maximum accuracy obtained during this stage was 77%. In the feedback phase, the volunteer was able to activate FES through brain–computer interface (BCI) in two tests (defined as Test 1 and Test 2) with the same procedure in different days. The closed-loop force control was tested with the setpoint of 2 kgf and 2.5 kgf and proved to be stable on both tests, successfully turning off the FES using the fatigue threshold from the MMG signal, being the main contribution of this work. Universidade Estadual Paulista Júlio Mesquita Filho (UNESP) Faculdade de Engenharia Campus Ilha Solteira, Av. Brasil Sul, 56 Universidade Tecnológica Federal do Paraná (UTFPR), Marcílio Dias, 635 Universidade Tecnológica Federal do Paraná (UTFPR), Avenida Sete de Setembro 3165 Universidade Estadual de Londrina (UEL) – Departamento de Anatomia Laboratório de Engenharia Neural e de Reabilitação, Rodovia Celso Garcia Cid – Pr 445, Km 380 Instituto Senai de Tecnologia da Informação e Comunicação (ISTIC) Laboratório de Sistemas Eletrônicos - Embarcados e de Potência IoT e Manufatura 4.0, Rua Belém 844 Universidade Estadual Paulista Júlio Mesquita Filho (UNESP) Faculdade de Engenharia Campus Ilha Solteira, Av. Brasil Sul, 56

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70fec0f73bbb946663180cd01679c641Test

المؤلفون: John McLinden, Sarah M Hosni, Yalda Shahriari, Seyyed Bahram Borgheai

المصدر: IEEE Transactions on Neural Systems and Rehabilitation Engineering. 28:3063-3073

مصطلحات موضوعية: Support Vector Machine, Computer science, Biomedical Engineering, Electroencephalography, 050105 experimental psychology, Data-driven, 03 medical and health sciences, 0302 clinical medicine, Motor imagery, Discriminative model, Internal Medicine, medicine, Humans, 0501 psychology and cognitive sciences, Brain–computer interface, Spectroscopy, Near-Infrared, medicine.diagnostic_test, business.industry, General Neuroscience, Subject specific, Amyotrophic Lateral Sclerosis, 05 social sciences, Rehabilitation, Pattern recognition, Support vector machine, Brain-Computer Interfaces, Imagination, Artificial intelligence, Functional magnetic resonance imaging, business, 030217 neurology & neurosurgery

الوصف: Objective: Functional near-infrared spectroscopy (fNIRS) has recently gained momentum in research on motor-imagery (MI)-based brain-computer interfaces (BCIs). However, strikingly, most of the research effort is primarily devoted to enhancing fNIRS-based BCIs for healthy individuals. The ability of patients with amyotrophic lateral sclerosis (ALS), among the main BCI end-users to utilize fNIRS-based hemodynamic responses to efficiently control an MI-based BCI, has not yet been explored. This study aims to quantify subject-specific spatio-temporal characteristics of ALS patients’ hemodynamic responses to MI tasks, and to investigate the feasibility of using these responses as a means of communication to control a binary BCI. Methods: Hemodynamic responses were recorded using fNIRS from eight patients with ALS while performing MI-Rest tasks. The generalized linear model (GLM) analysis was conducted to statistically estimate and evaluate individualized spatial activation. Selected channel sets were statistically optimized for classification. Subject-specific discriminative features, including a proposed data-driven estimated coefficient obtained from GLM, and optimized classification parameters were identified and used to further evaluate the performance using a linear support vector machine (SVM) classifier. Results: Inter-subject variations were observed in spatio-temporal characteristics of patients’ hemodynamic responses. Using optimized classification parameters and feature sets, all subjects could successfully use their MI hemodynamic responses to control a BCI with an average classification accuracy of 85.4% ± 9.8%. Significance: Our results indicate a promising application of fNIRS-based MI hemodynamic responses to control a binary BCI by ALS patients. These findings highlight the importance of subject-specific data-driven approaches for identifying discriminative spatio-temporal characteristics for an optimized BCI performance.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::484c4b41b4ca5c7387d3136f6dbf4c3aTest
https://doi.org/10.1109/tnsre.2020.3038717Test

المؤلفون: Andrzej Cichocki, Jing Jin, Shurui Li, Yangyang Miao, Ian Daly, Chang Liu, Xingyu Wang

المصدر: IEEE Transactions on Neural Systems and Rehabilitation Engineering. 28:2153-2163

مصطلحات موضوعية: Wilcoxon signed-rank test, Computer science, Biomedical Engineering, 02 engineering and technology, Electroencephalography, 03 medical and health sciences, 0302 clinical medicine, Motor imagery, 0202 electrical engineering, electronic engineering, information engineering, Internal Medicine, medicine, Humans, Brain–computer interface, medicine.diagnostic_test, Computers, business.industry, General Neuroscience, Rehabilitation, Brain, Signal Processing, Computer-Assisted, Pattern recognition, Brain-Computer Interfaces, Frequency domain, Imagination, 020201 artificial intelligence & image processing, Noise (video), Artificial intelligence, business, Bispectrum, 030217 neurology & neurosurgery, Communication channel

الوصف: The performance of motor imagery (MI) based Brain-computer interfacing (BCI) is easily affected by noise and redundant information that exists in the multi-channel electroencephalogram (EEG). To solve this problem, many temporal and spatial feature based channel selection methods have been proposed. However, temporal and spatial features do not accurately reflect changes in the power of the oscillatory EEG. Thus, spectral features of MI-related EEG signals may be useful for channel selection. Bispectrum analysis is a technique developed for extracting non-linear and non-Gaussian information from non-linear and non-Gaussian signals. The features extracted from bispectrum analysis can provide frequency domain information about the EEG. Therefore, in this study, we propose a bispectrum-based channel selection (BCS) method for MI-based BCI. The proposed method uses the sum of logarithmic amplitudes (SLA) and the first order spectral moment (FOSM) features extracted from bispectrum analysis to select EEG channels without redundant information. Three public BCI competition datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) were used to validate the effectiveness of our proposed method. The results indicate that our BCS method outperforms use of all channels (83.8% vs 69.4%, 86.3% vs 82.9% and 77.8% vs 68.2%, respectively). Furthermore, compared to the other state-of-the-art methods, our BCS method also can achieve significantly better classification accuracies for MI-based BCI (Wilcoxon signed test, p < 0.05).

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::274320692c39f6cea31ad51e6ead97d4Test
https://doi.org/10.1109/tnsre.2020.3020975Test

المؤلفون: Tao Yang, Jacob Jiexun Liao, Rosa Qi Yue So, Joy Jiayu Luo, Matthew Chin Heng Chua

المصدر: Brain-Computer Interfaces. 7:47-56

مصطلحات موضوعية: medicine.diagnostic_test, Computer science, business.industry, Deep learning, Biomedical Engineering, Image processing, Pattern recognition, Video processing, Electroencephalography, Signal, Convolutional neural network, Human-Computer Interaction, Behavioral Neuroscience, Motor imagery, medicine, Spatial ecology, Artificial intelligence, Electrical and Electronic Engineering, business

الوصف: An emerging idea in electroencephalogram motor-imagery (EEG-MI) classification is the ‘EEG-as-image’ approach. It aims to capture local EEG signal dynamics by preserving the spatial relationships o...

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::a2f24f785f648e0eb91802f6e8eec156Test
https://doi.org/10.1080/2326263x.2020.1801112Test

المؤلفون: Shixin Ren, Weiqun Wang, Zeng-Guang Hou, Weiguo Shi, Xu Liang, Jiaxing Wang

المصدر: IEEE Transactions on Neural Systems and Rehabilitation Engineering. 28:1846-1855

مصطلحات موضوعية: medicine.medical_specialty, Computer science, 0206 medical engineering, Biomedical Engineering, 02 engineering and technology, Electroencephalography, Virtual reality, 03 medical and health sciences, 0302 clinical medicine, Physical medicine and rehabilitation, Motor imagery, Internal Medicine, medicine, Humans, Functional electrical stimulation, Neurorehabilitation, Brain–computer interface, medicine.diagnostic_test, Computers, General Neuroscience, Rehabilitation, Virtual Reality, Brain, 020601 biomedical engineering, Electric Stimulation, Visualization, Lower Extremity, Brain-Computer Interfaces, Imagination, F1 score, 030217 neurology & neurosurgery

الوصف: Motor imagery based brain-computer interface (MI-BCI) has been studied for improvement of patients’ motor function in neurorehabilitation and motor assistance. However, the difficulties in performing imagery tasks limit its application. To overcome the limitation, an enhanced MI-BCI based on functional electrical stimulation (FES) and virtual reality (VR) is proposed in this study. On one hand, the FES is used to stimulate the subjects’ lower limbs before their imagination to make them experience the muscles’ contraction and improve their attention on the lower limbs, by which it is supposed that the subjects’ motor imagery (MI) abilities can be enhanced. On the other hand, a ball-kicking movement scenario from the first-person perspective is designed to provide visual guidance for performing MI tasks. The combination of FES and VR can be used to reduce the difficulties in performing MI tasks and improve classification accuracy. Finally, the comparison experiments were conducted on twelve healthy subjects to validate the performance of the enhanced MI-BCI. The results show that the classification performance can be improved significantly by using the proposed MI-BCI in terms of the classification accuracy (ACC), the area under the curve (AUC) and the F1 score (paired t-test, ${p} ).

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5118f4ac576c1d60064fda99d7a37bf4Test
https://doi.org/10.1109/tnsre.2020.3001990Test

المؤلفون: Laurent Bougrain, Oleksii Avilov, Anton Popov, Sébastien Rimbert

المساهمون: National Technical University of Ukraine 'Kyiv Polytechnic Institute' [Kiev], Analysis and modeling of neural systems by a system neuroscience approach (NEUROSYS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Oleksii Avilov was supported by scholarship from the French Embassy to Ukraine while working on this topic at the NEUROSYS team at LORIA (Université de Lorraine/CNRS/Inria), Nancy, France. Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (https://www.grid5000.frTest)., Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

المصدر: IEEE Transactions on Biomedical Engineering
IEEE Transactions on Biomedical Engineering, Institute of Electrical and Electronics Engineers, 2021, pp.10. ⟨10.1109/TBME.2021.3064794⟩
IEEE Transactions on Biomedical Engineering, 2021, pp.10. ⟨10.1109/TBME.2021.3064794⟩

مصطلحات موضوعية: 0209 industrial biotechnology, Computer science, Biomedical Engineering, motor imagery AAGA: accidental awareness during general anesthesia, 02 engineering and technology, Intention, Electroencephalography, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Intraoperative Awareness, 020901 industrial engineering & automation, Motor imagery, median nerve stimulation, Deep Learning, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], intraoperative awareness during general anesthesia, 0202 electrical engineering, electronic engineering, information engineering, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Functional electrical stimulation, Humans, Brain-computer interface (BCI), Artificial neural network, medicine.diagnostic_test, Median nerve stimulation, business.industry, electroencephalogram (EEG), Deep learning, [SCCO.NEUR]Cognitive science/Neuroscience, Pattern recognition, Linear discriminant analysis, Median nerve, medicine.anatomical_structure, machine learning, Frontal lobe, Brain-Computer Interfaces, Imagination, 020201 artificial intelligence & image processing, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithms, Motor cortex

الوصف: International audience; Objective: This article shows the interest in deep learning techniques to detect motor imagery (MI) from raw electroencephalographic (EEG) signals when a functional electrical stimulation is added or not. Impacts of electrode montages and bandwidth are also reported. The perspective of this work is to improve the detection of intraoperative awareness during general anesthesia. Methods: Various architectures of EEGNet were investigated to optimize MI detection. They have been compared to the state-of-the-art classifiers in Brain-Computer Interfaces (based on Riemannian geometry, linear discriminant analysis), and other deep learning architectures (deep convolution network, shallow convolutional network). EEG data were measured from 22 participants performing motor imagery with and without median nerve stimulation. Results: The proposed architecture of EEGNet reaches the best classification accuracy (83.2%) and false-positive rate (FPR 19.0%) for a setup with only six electrodes over the motor cortex and frontal lobe and for an extended 4-38 Hz EEG frequency range while the subject is being stimulated via a median nerve. Configurations with a larger number of electrodes result in higher accuracy (94.5%) and FPR (6.1%) for 128 electrodes (and respectively 88.0% and 12.9% for 13 electrodes).Conclusion: The present work demonstrates that using an extended EEG frequency band and a modified EEGNet deep neural network increases the accuracy of MI detection when used with as few as 6 electrodes which include frontal channels. Significance: The proposed method contributes to the development of Brain-Computer Interface systems based on MI detection from EEG.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a8f3237ecaea92e273feb58535b7c0d0Test
https://pubmed.ncbi.nlm.nih.gov/33687833Test