Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface

Authors:
Shang-Ming Zhou;John Q. Gan;Francisco Sepulveda
Affiliations:
Centre for Computational Intelligence, School of Computing, De Montfort University, Leicester LE1 9BH, UK;Department of Computing and Electronic Systems, University of Essex, Colchester CO4 3SQ, UK;Department of Computing and Electronic Systems, University of Essex, Colchester CO4 3SQ, UK
Venue:
Information Sciences: an International Journal
Year:
2008

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Abstract

In order to characterize the non-Gaussian information contained within the EEG signals, a new feature extraction method based on bispectrum is proposed and applied to the classification of right and left motor imagery for developing EEG-based brain-computer interface systems. The experimental results on the Graz BCI data set have shown that based on the proposed features, a LDA classifier, SVM classifier and NN classifier outperform the winner of the BCI 2003 competition on the same data set in terms of either the mutual information, the competition criterion, or misclassification rate.