Yang, Jianhua, Singh, Harsimrat, Hines, Evor, Schlaghecken, Friederike, Iliescu, Daciana, Leeson, Mark S. and Stocks, Nigel G.. (2012) Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach. Artificial Intelligence In Medicine, Volume 55 (Number 2). pp. 117-126. ISSN 1873-2860

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Abstract

We demonstrate that GNMM is able to perform effective channel selections/reductions, which not only reduces the difficulty of data collection, but also greatly improves the generalization of the classifier. An important step that affects the effectiveness of GNMM is the pre-processing method. In this paper, we also highlight the importance of choosing an appropriate time window position.

Item Type:	Journal Article
Subjects:	R Medicine > R Medicine (General) R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Science > Engineering Faculty of Science > Psychology
Journal or Publication Title:	Artificial Intelligence In Medicine
Publisher:	Elsevier BV
ISSN:	1873-2860
Official Date:	June 2012
Dates:	Date Event June 2012 Published February 2012 Accepted 18 November 2010 Submitted
Volume:	Volume 55
Number:	Number 2
Number of Pages:	10
Page Range:	pp. 117-126
Identifier:	10.1016/j.artmed.2012.02.001
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access
Funder:	Economic and Social Research Council (Great Britain) (ESRC), Warwick Postgraduate Research Fellowship (WPRF), UK Overseas Research Students Awards Scheme (ORSAS), Warwick Institute of Advanced Study (IAS)
Grant number:	RES-000-22-1841 (ESRC)
URI:	http://wrap.warwick.ac.uk/id/eprint/46498

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