Bhattacharyya bound based channel selection for classification of motor imageries in EEG signals

Authors:
Lin He;Zhuliang Yu;Zhenghui Gu;Yuanqing Li
Affiliations:
College of Automation Science and Technology, South China University of Technology, Guangzhou;College of Automation Science and Technology, South China University of Technology, Guangzhou;College of Automation Science and Technology, South China University of Technology, Guangzhou;College of Automation Science and Technology, South China University of Technology, Guangzhou
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

Citing 4
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Abstract

In EEG-based brain computer interfaces (BCIs), channel selection is important for the classification of mental task, such as motor imagery. In this paper, a channel selection method is presented for motor imagery. The Bhattacharyya bound of common spatial pattern (CSP) features is used as the optimal index, and a fast sequential forward search is applied to find the optimal combination of channels. The data analysis results show the improvement of classification accuracy.