Effect of feature and channel selection on EEG classification.

Full Abstract

In this paper, we evaluate the significance of feature and channel selection on EEG classification. The selection process is performed by searching the feature/channel space using genetic algorithm, and evaluating the importance of subsets using a linear support vector machine classifier. Three approaches have been considered: (i) selecting a subset of features that will be used to represent a specified set of channels, (ii) selecting channels that are each represented by a specified set of features, and (iii) selecting individual features from different channels. When applied to a brain-computer interface (BCI) problem, results indicate that improvement in classification accuracy can be achieved by considering the correct combination of channels and features.

Author information

Author/s: Al-Ani, Ahmed (A); Al-Sukker, Akram (A);

Affiliation: Fac. of Eng., Univ. of Technol., Sydney, NSW 2207, Australia. ahmed(-atsign-)eng.uts.edu.au

Journal and publication information

Publication Type: Journal Article

Journal: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference (Conf Proc IEEE Eng Med Biol Soc), published in United States. (Language: eng)

Reference: 2006-; vol 1 (issue ) : pp 2171-4

Dates: Created 2007/10/23; Completed 2008/03/17;

PMID: 17946093, status: MEDLINE (last retrieval date: 2/18/2009, IMS Date: 18 Feb 2009 00:00:00)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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