Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface
Date
2008-03Subject
brain-computer interfaces classification electroencephalogram (EEG) feature extraction higher-order statistics bispectrum
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Show full item recordAbstract
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. © 2007 Elsevier Inc. All rights reserved.
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Publisher
Elsevier BV
Journal
Information Sciences
Volume
178
Issue
6
Pagination
1629-1640
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