An EEG Coherence Based Method Used for Mental Tasks Classification

In this paper a new coherence based method to extract the appropriate EEG features for a five mental tasks classification problem is proposed. The new introduced method has the advantage of using an adaptive new technique that models the EEG signal using the frequency information obtained by employing the coherence function to each EEG recording channel. The adaptive attribute of the technique is due to both, to the amplitude and, respective, to the phase adaptive processes used to model the EEG signal. Another specificity of the new modeling technique is given by the fact of exploiting the nonlinear dynamics of the brain system; this is reflected in the particular spectral mixing of the fundamental spectral components obtained first, by using the coherence function. Finally, to conclude the obtained results in comparison with the results reported in the literature, by using this new approach the classification rate was noticeably improved.