Toward fewer EEG channels and better feature extractor of non-motor imagery mental tasks classification for a wheelchair thought controller

Chai, R; Ling, SH; Hunter, GP; Nguyen, HT

Toward fewer EEG channels and better feature extractor of non-motor imagery mental tasks classification for a wheelchair thought controller

Chai, R Ling, SH Hunter, GP Nguyen, HT

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Publisher:: IEEE Xplore
Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2012, pp. 5266 - 5269
Issue Date:: 2012-12-14

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Field	Value	Language
dc.contributor.author	Chai, R	en_US
dc.contributor.author	Ling, SH	en_US
dc.contributor.author	Hunter, GP	en_US
dc.contributor.author	Nguyen, HT	en_US
dc.date	2012-08-28	en_US
dc.date.issued	2012-12-14	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2012, pp. 5266 - 5269	en_US
dc.identifier.isbn	9781424441198	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119572
dc.description.abstract	This paper presents a non-motor imagery tasks classification electroencephalography (EEG) based brain computer interface (BCI) for wheelchair control. It uses only two EEG channels and a better feature extractor to improve the portability and accuracy in the practical system. In addition, two different features extraction methods, power spectral density (PSD) and Hilbert Huang Transform (HHT) energy are compared to find a better method with improved classification accuracy using a Genetic Algorithm (GA) based neural network classifier. The results from five subjects show that using the original eight channels with three tasks, accuracy between 76% and 85% is achieved. With only two channels in combination with the best chosen task using a PSD feature extractor, the accuracy is reduced to between 65% and 79%. However, the HHT based method provides an improved accuracy between 70% and 84% for the classification of three discriminative tasks using two EEG channels. © 2012 IEEE.	en_US
dc.publisher	IEEE Xplore	en_US
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	en_US
dc.relation.ispartof	International Conference of the IEEE Engineering in Medicine and Biology Society	en_US
dc.relation.isbasedon	10.1109/EMBC.2012.6347182	en_US
dc.rights	© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Toward fewer EEG channels and better feature extractor of non-motor imagery mental tasks classification for a wheelchair thought controller	en_US
dc.type	Conference Proceeding
utslib.location.activity	San Diego, CA, USA	en_US
utslib.for	0903 Biomedical Engineering	en_US
pubs.embargo.period	Not known	en_US
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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access	*
pubs.declined	2017-11-01T11:42:32.111+1100
pubs.consider-herdc	false	en_US
pubs.deleted	2017-11-01T11:42:32.111+1100
pubs.finish-date	2012-09-01	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2012-08-28	en_US

Abstract:

This paper presents a non-motor imagery tasks classification electroencephalography (EEG) based brain computer interface (BCI) for wheelchair control. It uses only two EEG channels and a better feature extractor to improve the portability and accuracy in the practical system. In addition, two different features extraction methods, power spectral density (PSD) and Hilbert Huang Transform (HHT) energy are compared to find a better method with improved classification accuracy using a Genetic Algorithm (GA) based neural network classifier. The results from five subjects show that using the original eight channels with three tasks, accuracy between 76% and 85% is achieved. With only two channels in combination with the best chosen task using a PSD feature extractor, the accuracy is reduced to between 65% and 79%. However, the HHT based method provides an improved accuracy between 70% and 84% for the classification of three discriminative tasks using two EEG channels. © 2012 IEEE.

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http://hdl.handle.net/10453/119572