Selection of an efficient feature space for EEG-based mental task discrimination

The aim of this paper is to contribute toward exploring an optimal feature space for discriminating mental tasks. Empirical mode decomposition (EMD) algorithm seems useful for designing such a feature space. The adjustment of nonlinear and non-stationary properties of the EEG signals with this algorithm and the successful application of this approach together biomedical signal processing problems encourage us to examine a variety of statistical and spectral measures within the EMD space as the adapted features. In this sense, as a measure of complexity, the Lempel–Ziv algorithm is utilized within the framework of the EMD algorithm. A modified form of the Lempel–Ziv complexity algorithm is then proposed. The features derived from the modified algorithm outperform the other features individually. By combining the modified Lempel–Ziv features with the other adopted features, in average, 97.78% classification accuracy is achieved for different subjects. It is concluded that the EMD–LZ kernel allows for achieving of better performances in classifying mental tasks than the results obtained with other methods.