Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface - Shang-ming Zhou (Information Sciences, 2008)
This paper proposes a new feature extraction method to characterize the non-Gaussian information contained within the EEG signals using bispectrum. Moreover, the proposed method is 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 brain-computer interface (BCI) 2003 competition on the same data set in terms of either the mutual information, the competition criterion, or misclassification rate.