Comparison of EEG pattern recognition of motor imagery for finger movement classification

Anam, K; Nuh, M; Al-Jumaily, A

Comparison of EEG pattern recognition of motor imagery for finger movement classification

Anam, K Nuh, M Al-Jumaily, A

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2019 6th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2020, 00, pp. 24-27
Issue Date:: 2020

Closed Access

	Filename	Description	Size
	08977037.pdf		509.63 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Anam, K
dc.contributor.author	Nuh, M
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463
dc.date	2019-09-18
dc.date.accessioned	2021-05-20T01:10:16Z
dc.date.available	2021-05-20T01:10:16Z
dc.date.issued	2020
dc.identifier.citation	2019 6th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2020, 00, pp. 24-27
dc.identifier.isbn	9786020737287
dc.identifier.issn	2407-439X
dc.identifier.uri	http://hdl.handle.net/10453/148993
dc.description.abstract	© 2019, Institute of Advanced Engineering and Science. All rights reserved. The detection of a hand movement beforehand can be a beneficent tool to control a prosthetic hand for upper extremity rehabilitation. To be able to achieve smooth control, the intention detection is acquired from the human body, especially from brain signal or electroencephalogram (EEG) signal. However, many constraints hamper the development of this brain-computer interface (BCI), especially for finger movement detection. Most of the researchers have focused on the detection of the left and right-hand movement. This article presents the comparison of various pattern recognition method for recognizing five individual finger movements, i.e., the thumb, index, middle, ring, and pinky finger movements. The EEG pattern recognition utilized common spatial pattern (CSP) for feature extraction. As for the classifier, four classifiers, i.e., random forest (RF), support vector machine (SVM), k-nearest neighborhood (kNN), and linear discriminant analysis (LDA) were tested and compared to each other. The experimental results indicated that the EEG pattern recognition with RF achieved the best accuracy of about 54%. Other published publication reported that the classification of the individual finger movement is still challenging and need more efforts to achieve better performance.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2019 6th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
dc.relation.ispartof	International Conference on Electrical Engineering, Computer Science and Informatics
dc.relation.isbasedon	10.23919/EECSI48112.2019.8977037
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Comparison of EEG pattern recognition of motor imagery for finger movement classification
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Bandung, Indonesia
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/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-05-20T01:10:14Z
pubs.finish-date	2019-09-20
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-09-18
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

© 2019, Institute of Advanced Engineering and Science. All rights reserved. The detection of a hand movement beforehand can be a beneficent tool to control a prosthetic hand for upper extremity rehabilitation. To be able to achieve smooth control, the intention detection is acquired from the human body, especially from brain signal or electroencephalogram (EEG) signal. However, many constraints hamper the development of this brain-computer interface (BCI), especially for finger movement detection. Most of the researchers have focused on the detection of the left and right-hand movement. This article presents the comparison of various pattern recognition method for recognizing five individual finger movements, i.e., the thumb, index, middle, ring, and pinky finger movements. The EEG pattern recognition utilized common spatial pattern (CSP) for feature extraction. As for the classifier, four classifiers, i.e., random forest (RF), support vector machine (SVM), k-nearest neighborhood (kNN), and linear discriminant analysis (LDA) were tested and compared to each other. The experimental results indicated that the EEG pattern recognition with RF achieved the best accuracy of about 54%. Other published publication reported that the classification of the individual finger movement is still challenging and need more efforts to achieve better performance.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/148993