Evaluation of feature selection methods for improved EEG classification

Alsukker, A; Al-Ani, A

Evaluation of feature selection methods for improved EEG classification

Alsukker, A Al-Ani, A

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Publication Type:: Conference Proceeding
Citation:: ICBPE 2006 - Proceedings of the 2006 International Conference on Biomedical and Pharmaceutical Engineering, 2006, pp. 146 - 151
Issue Date:: 2006-12-01

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Field	Value	Language
dc.contributor.author	Alsukker, A	en_US
dc.contributor.author	Al-Ani, A https://orcid.org/0000-0002-8092-8954	en_US
dc.date.issued	2006-12-01	en_US
dc.identifier.citation	ICBPE 2006 - Proceedings of the 2006 International Conference on Biomedical and Pharmaceutical Engineering, 2006, pp. 146 - 151	en_US
dc.identifier.isbn	8190426249	en_US
dc.identifier.isbn	9788190426244	en_US
dc.identifier.uri	http://hdl.handle.net/10453/7164
dc.description.abstract	this paper compares several methods for feature selection used in EEG classification. Sequential, heuristics and population-based search methods are compared according to their efficiency and computational cost. A support vector machine classifier has been used to compare accuracies. Effect of the size of feature space has been explored by changing the total number of variables between 27 and 168. Experiments have been conducted to select channels as well as to select individual features from different channels. © 2006 Research Publishing Services.	en_US
dc.relation.ispartof	ICBPE 2006 - Proceedings of the 2006 International Conference on Biomedical and Pharmaceutical Engineering	en_US
dc.relation.isbasedon	10.1109/ICBPE.2006.348572	en_US
dc.title	Evaluation of feature selection methods for improved EEG classification	en_US
dc.type	Conference Proceeding
utslib.for	090399 Biomedical Engineering not elsewhere classified	en_US
dc.location.activity	Singapore	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.publication-status	Published	en_US

Abstract:

this paper compares several methods for feature selection used in EEG classification. Sequential, heuristics and population-based search methods are compared according to their efficiency and computational cost. A support vector machine classifier has been used to compare accuracies. Effect of the size of feature space has been explored by changing the total number of variables between 27 and 168. Experiments have been conducted to select channels as well as to select individual features from different channels. © 2006 Research Publishing Services.

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http://hdl.handle.net/10453/7164