Motor imagery movements detection of EEG signals using statistical features in the Dual Tree Complex Wavelet Transform domain

In this paper, a statistical method has been proposed to identify motor imagery left and right hand movements from electroencephalogram (EEG) signals in the Dual Tree Complex Wavelet Transform (DTCWT) domain. The total experiment is carried out with the publicly available benchmark BCI-competition 2003 Graz motor imagery dataset. First, the EEG signals are decomposed into several bands of real and imaginary coefficients, and then, some statistical features like norm entropy and standard deviation have been calculated. From the one way ANOVA analysis, these features have been shown to be promising to distinguish various kinds of EEG signals. Various types of classifiers have been developed to realize the discrimination among the EEG signals. Among various types of classifiers, K-nearest neighbor (KNN)-based classifiers have been shown to provide a good accuracy of 90.36% which is shown to be better than several existing techniques.