Motor imagery EEG classification based on flexible analytic wavelet transform

Abstract Motor imagery electroencephalogram (MI-EEG) based brain-computer interface (BCI) is a burgeoning auxiliary means to realize rehabilitation therapy. One of the major concerns in MI-EEG based BCI is to have an accurate classification, and effective and fast feature extraction is the key to build a successful MI-EEG classification model. In this paper, a novel classification system for MI-EEG signals is proposed based on flexible analytic wavelet transform (FAWT). The filtered MI-EEG signals are firstly subjected to the FAWT to obtain sub-bands, and time-frequency features are calculated from the sub-bands. MDS is then adopted to reduce the dimension of the extracted features, and principal component analysis (PCA), kernel principal component analysis (KPCA), locally linear embedding (LLE) and Laplacian Eigenmaps (LE) are utilized as comparison. Finally, linear discriminant analysis (LDA) is utilized to complete the classification of left-hand (LH) and right-hand (RH) MI-EEG signals. The proposed method is experimentally validated on BCI Competition II Data Set III (BCI Dataset III) and BCI Competition III Data Set IIIb (BCI Dataset IIIb). As a result, the combined method of FAWT, MDS attains the maximal mutual information (MaI) of 0.95 and the maximum accuracy (ACC) of 94.29% using BCI Dataset III, and the mean of the maximal MaI steepness of 0.3740 using BCI Dataset IIIb. The proposed method yields better performance in comparison to the existing methods. Overall, the effectiveness of the proposed approach suggests that it can be a worthwhile and promising method for a MI-EEG based BCI system.