Channel selection by Rayleigh coefficient maximization based genetic algorithm for classifying single-trial motor imagery EEG

Authors:
Lin He;Youpan Hu;Yuanqing Li;Daoli Li
Affiliations:
-;-;-;-
Venue:
Neurocomputing
Year:
2013

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Abstract

While common spatial pattern may be the most widely used feature for discriminating motor imagery based EEG signals, Rayleigh coefficient maximization enable us to have one more effective. However, such a feature is often deteriorated by redundant electrode channels which may result in low classification accuracy, extra subsequent computational load and difficulty in understanding which part of the brain relates to classification-relevant activity. In this paper, we present a channel selection method to deal with these problems, in which an improved genetic algorithm based on the Rayleigh coefficient feature is conducted to determine the optimal subset of channels. Experiment results on two motor imagery EEG datasets verify that our method is effective in channel selection for classifying motor imagery EEG signals.