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An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation

Anirban Chowdhury, Haider Raza, Yogesh Kumar Meena, Ashish Dutta, Girijesh Prasad

Journal of Neuroscience Methods, Volume: 312, Pages: 1 - 11

Swansea University Author: Yogesh Kumar Meena

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DOI (Published version): 10.1016/j.jneumeth.2018.11.010

Abstract

Background Corticomuscular coupling has been investigated for long, to find out the underlying mechanisms behind cortical drives to produce different motor tasks. Although important in rehabilitation perspective, the use of corticomuscular coupling for driving brain-computer interface (BCI)-based ne...

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Published in: Journal of Neuroscience Methods
ISSN: 01650270
Published: 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa46245
first_indexed 2018-12-06T14:27:36Z
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spelling 2023-06-23T18:22:52.6517745 v2 46245 2018-12-06 An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation 99fa72c8a55321a225c0a5abf0955585 Yogesh Kumar Meena Yogesh Kumar Meena true false 2018-12-06 MACS Background Corticomuscular coupling has been investigated for long, to find out the underlying mechanisms behind cortical drives to produce different motor tasks. Although important in rehabilitation perspective, the use of corticomuscular coupling for driving brain-computer interface (BCI)-based neurorehabilitation is much ignored. This is primarily due to the fact that the EEG-EMG coherence popularly used to compute corticomuscular coupling, fails to produce sufficient accuracy in single-trial based prediction of motor tasks in a BCI system. New Method In this study, we have introduced a new corticomuscular feature extraction method based on the correlation between band-limited power time-courses (CBPT) associated with EEG and EMG. 16 healthy individuals and 8 hemiplegic patients participated in a BCI-based hand orthosis triggering task, to test the performance of the CBPT method. The healthy population was equally divided into two groups; one experimental group for CBPT-based BCI experiment and another control group for EEG-EMG coherence based BCI experiment. Results The classification accuracy of the CBPT-based BCI system was found to be 92.81± 2.09 % for the healthy experimental group and 84.53± 4.58 % for the patients’ group. Comparison with existing method The CBPT method significantly (p−value &#60; 0.05) outperformed the conventional EEG-EMG coherence method in terms of classification accuracy. Conclusions The experimental results clearly indicate that the EEG-EMG CBPT is a better alternative as a corticomuscular feature to drive a BCI system. Additionally, it is also feasible to use the proposed method to design BCI-based robotic neurorehabilitation paradigms. Journal Article Journal of Neuroscience Methods 312 1 11 01650270 Corticomuscular-Coherence (CMC), correlation between band-limited powertime-courses (CBPT), Electroencephalogram (EEG), Electromyogram (EMG), HybridBrain-computer interface (h-BCI), Hand Orthosis, Neurorehabilitation. 15 1 2019 2019-01-15 10.1016/j.jneumeth.2018.11.010 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2023-06-23T18:22:52.6517745 2018-12-06T12:06:19.8994289 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering Anirban Chowdhury 1 Haider Raza 2 Yogesh Kumar Meena 3 Ashish Dutta 4 Girijesh Prasad 5 0046245-15012019105032.pdf Plainpeerreviewedacceptedversionv2.pdf 2019-01-15T10:50:32.3730000 Output 7735620 application/pdf Accepted Manuscript true 2019-11-16T00:00:00.0000000 Released under the terms of a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND). true eng
title An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation
spellingShingle An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation
Yogesh Kumar Meena
title_short An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation
title_full An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation
title_fullStr An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation
title_full_unstemmed An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation
title_sort An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation
author_id_str_mv 99fa72c8a55321a225c0a5abf0955585
author_id_fullname_str_mv 99fa72c8a55321a225c0a5abf0955585_***_Yogesh Kumar Meena
author Yogesh Kumar Meena
author2 Anirban Chowdhury
Haider Raza
Yogesh Kumar Meena
Ashish Dutta
Girijesh Prasad
format Journal article
container_title Journal of Neuroscience Methods
container_volume 312
container_start_page 1
publishDate 2019
institution Swansea University
issn 01650270
doi_str_mv 10.1016/j.jneumeth.2018.11.010
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Biomedical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Biomedical Engineering
document_store_str 1
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description Background Corticomuscular coupling has been investigated for long, to find out the underlying mechanisms behind cortical drives to produce different motor tasks. Although important in rehabilitation perspective, the use of corticomuscular coupling for driving brain-computer interface (BCI)-based neurorehabilitation is much ignored. This is primarily due to the fact that the EEG-EMG coherence popularly used to compute corticomuscular coupling, fails to produce sufficient accuracy in single-trial based prediction of motor tasks in a BCI system. New Method In this study, we have introduced a new corticomuscular feature extraction method based on the correlation between band-limited power time-courses (CBPT) associated with EEG and EMG. 16 healthy individuals and 8 hemiplegic patients participated in a BCI-based hand orthosis triggering task, to test the performance of the CBPT method. The healthy population was equally divided into two groups; one experimental group for CBPT-based BCI experiment and another control group for EEG-EMG coherence based BCI experiment. Results The classification accuracy of the CBPT-based BCI system was found to be 92.81± 2.09 % for the healthy experimental group and 84.53± 4.58 % for the patients’ group. Comparison with existing method The CBPT method significantly (p−value &#60; 0.05) outperformed the conventional EEG-EMG coherence method in terms of classification accuracy. Conclusions The experimental results clearly indicate that the EEG-EMG CBPT is a better alternative as a corticomuscular feature to drive a BCI system. Additionally, it is also feasible to use the proposed method to design BCI-based robotic neurorehabilitation paradigms.
published_date 2019-01-15T04:31:43Z
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