Identification of Motor Imagery Movements from EEG Signals Using Automatically Selected Features in the Dual Tree Complex Wavelet Transform Domain

Abstract

The decoding of human brain electrical functions by electroencephalogram (EEG) signal is the most important step in brain computer interface (BCI) based systems. So, in this paper, an automatic feature selection method has been proposed to classify imagery left and right hand movements from the EEG signals in the Dual Tree Complex Wavelet Transform domain. First, the EEG signals are decomposed into several bands of real and imaginary coefficients and then, some statistical features like Shannon entropy and variance have been calculated. These features are combined into a single feature space and after that optimal features have been selected automatically imposing some feature selection criteria from this combined feature space. The selected features have been shown to be promising to distinguish different kinds of EEG signals by statistical hypothesis testing (e.g., one way ANOVA) as well as graphical analysis (e.g., scatter plots, box plots). Finally, k-nearest neighbor based classifiers are developed using these selected features to identify left and right hand imagery movements. A mean accuracy of 90.00% is achieved in publicly available BCI competition II Graz motor imagery data set which is shown to be better than some existing techniques.