An EEG Majority Vote Based BCI Classification System for Discrimination of Hand Motor Attempts in Stroke Patients

Gu, X; Cao, Z

An EEG Majority Vote Based BCI Classification System for Discrimination of Hand Motor Attempts in Stroke Patients

Gu, X Cao, Z

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Neural Information Processing, 2021, 1333, pp. 46-53
Issue Date:: 2021

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	Gu-Cao2020_Chapter_AnEEGMajorityVoteBasedBCIClass.pdf	Published version	1.02 MB	Adobe PDF	View/Open

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Field	Value	Language
dc.contributor.author	Gu, X
dc.contributor.author	Cao, Z https://orcid.org/0000-0003-3656-0328
dc.date	2020-11-18
dc.date.accessioned	2022-06-28T01:30:20Z
dc.date.available	2022-06-28T01:30:20Z
dc.date.issued	2021
dc.identifier.citation	Neural Information Processing, 2021, 1333, pp. 46-53
dc.identifier.isbn	9783030638221
dc.identifier.issn	1865-0929
dc.identifier.issn	1865-0937
dc.identifier.uri	http://hdl.handle.net/10453/158421
dc.description.abstract	Stroke patients have symptoms of cerebral functional disturbance that could aggressively impair patient’s physical mobility, such as hand impairments. Although rehabilitation training from external devices is beneficial for hand movement recovery, for initiating motor function restoration purposes, there are still valuable research merits for identifying the side of hands in motion. In this preliminary study, we used an electroencephalogram (EEG) dataset from 8 stroke patients, with each subject conducting 40 EEG trials of left motor attempts and 40 EEG trials of right motor attempts. Then, we proposed a majority vote based EEG classification system for identifying the side in motion. In specific, we extracted 1–50 Hz power spectral features as input for a series of well-known classification models. The predicted labels from these classification models were compared and a majority vote based method was applied, which determined the finalised predicted label. Our experiment results showed that our proposed EEG classification system achieved $$99.83 \pm 0.42 \% $$ accuracy, $$ 99.98 \pm 0.13\% $$ precision, $$ 99.66 \pm 0.84 \% $$ recall, and $$ 99.83 \pm 0.43\% $$ f-score, which outperformed the performance of single well-known classification models. Our findings suggest that the superior performance of our proposed majority vote based EEG classification system has the potential for stroke patients’ hand rehabilitation.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Neural Information Processing
dc.relation.ispartof	International Conference on Neural Information Processing
dc.relation.ispartofseries	Communications in Computer and Information Science
dc.relation.isbasedon	10.1007/978-3-030-63823-8_6
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	An EEG Majority Vote Based BCI Classification System for Discrimination of Hand Motor Attempts in Stroke Patients
dc.type	Conference Proceeding
utslib.citation.volume	1333
utslib.location.activity	Bangkok, Thailand
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-06-28T01:30:19Z
pubs.finish-date	2020-11-22
pubs.place-of-publication	Swizterland
pubs.publication-status	Published
pubs.start-date	2020-11-18
pubs.volume	1333
dc.location	Swizterland

Abstract:

Stroke patients have symptoms of cerebral functional disturbance that could aggressively impair patient’s physical mobility, such as hand impairments. Although rehabilitation training from external devices is beneficial for hand movement recovery, for initiating motor function restoration purposes, there are still valuable research merits for identifying the side of hands in motion. In this preliminary study, we used an electroencephalogram (EEG) dataset from 8 stroke patients, with each subject conducting 40 EEG trials of left motor attempts and 40 EEG trials of right motor attempts. Then, we proposed a majority vote based EEG classification system for identifying the side in motion. In specific, we extracted 1–50 Hz power spectral features as input for a series of well-known classification models. The predicted labels from these classification models were compared and a majority vote based method was applied, which determined the finalised predicted label. Our experiment results showed that our proposed EEG classification system achieved $$99.83 \pm 0.42 \% $$ accuracy, $$ 99.98 \pm 0.13\% $$ precision, $$ 99.66 \pm 0.84 \% $$ recall, and $$ 99.83 \pm 0.43\% $$ f-score, which outperformed the performance of single well-known classification models. Our findings suggest that the superior performance of our proposed majority vote based EEG classification system has the potential for stroke patients’ hand rehabilitation.

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http://hdl.handle.net/10453/158421