Brain-Computer Interface Design Based on Relative Wavelet Energy

The feature extraction method using relative wavelet energy(RWE) is investigated for a brain-computer interface(BCI) based on two different mental tasks,i.e.,the imaginary left and right hand movements.Discusses the computational method of RWE in depth,then RWE is used for the feature extraction of EEG signals with the support vector machine(SVM) used for classification.Classification accuracy and mutual information(MI) are taken as the evaluation criteria for BCI system.The off-line analysis results show that the maximum classification accuracy is 85.7% and maximum MI is 0.41 bit.Both are higher than the feature extraction characterized by the conventional adaptive autoregressive(AAR) coefficients.