Identification of arm movements using statistical features from EEG signals in wavelet packet domain

In this paper, a method to classify arm movements using statistical features of electroencephalogram (EEG) signals calculated from wavelet packet and Fourier transforms, has been proposed. The EEG signals are analyzed using bi-orthogonal wavelet packet family. Fourier transform is then applied to the corresponding detail coefficients and higher order statistical moment named kurtosis is calculated from the magnitude of the Fourier components. The features are shown to be distinguishable for the EEG signals of four different arm movements. K-nearest neighbor (KNN)-based classifiers are developed using these features to identify the arm movements, right hand forward and backward; left hand forward and backward. A mean accuracy of 92.84% is achieved which is shown to be better than some existing techniques.