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Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach.

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Yang, Jianhua, Singh, Harsimrat, Hines, Evor L., Schlaghecken, Friederike, Iliescu, Daciana, Leeson, Mark S., 1963- 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, Vol.55 (No.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
Date: June 2012
Volume: Vol.55
Number: No.2
Number of Pages: 10
Page Range: pp. 117-126
Identification Number: 10.1016/j.artmed.2012.02.001
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription 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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