A novel effective feature selection algorithm based on S-PCA and wavelet transform features in EEG signal classification

There are various methods to extract feature from EEG signals but the effective feature selection is an issue. In this paper, a novel effective feature selection based on Statistical-Principal Component Analysis (S-PCA) and wavelet transform (WT) features in medical and BCI application is proposed. In this method, we decompose the signals to six sub-bands by four mother wavelet (sym6, db5, bior1.5 and robio2.8). Then five features (such as the number of zero coefficients, the smallest and largest coefficients, the mean and standard deviation of coefficients) extract from each sub-band as feature vector. In this algorithm, S-PCA is used to select ten effective features from among WT features. Finally, we use KNN classifier and seven different signals of brain activities to evaluate the proposed method. The results indicate the improvement of the classification performance in comparison with current methods.