Brain Computer Interface (BCI) is a channel of communication between the human brain and an external device through brain electrical activity. In this paper, we extracted different features to boost the classification accuracy as well as the mutual information of BCI systems. The extracted features include the magnitude of the discrete Fourier transform and the wavelet coefficients for the EEG signals in addition to distance series values and invariant moments calculated for the reconstructed phase space of the EEG measurements. Different preprocessing, feature selection, and classification schemes were utilized to evaluate the performance of the proposed system for dataset III from BCI competition II. The maximum accuracy achieved was 90.7% while the maximum mutual information was 0.76 bit obtained using the distance series features.