Motor imagery movements classification using multivariate EMD and short time Fourier transform

In this paper, a novel method consisting of multivariate empirical mode decomposition (MEMD) and short time Fourier transform (STFT) is proposed to identify left and right imagery hand movements from electroencephalogram (EEG) signals. Experiments are carried out using the publicly available benchmark BCI competition 2003 Graz motor imagery data base of left and right hand movements. The EEG epochs are decomposed into multiple intrinsic mode functions (IMFs) by applying MEMD. The most significant mode is subjected to the short time Fourier transform; the peak and entropy of the magnitude spectrum are used as features representing the corresponding epoch. Extensive analysis is carried out using Kruskal-Wallis test, scatter plots, box plots and histograms to justify the employed features. Classification of the motor imagery movements is studied using the proposed features and various state of the art classifiers. The highest accuracy is achieved employing the k-nearest neighbor (kNN) classifier which is 90.00% and better than those of the several contemporary methods.