Application of empirical mode decomposition and Teager energy operator to EEG signals for mental task classification

This paper presents a novel method for mental task classification from EEG signals using Empirical Mode Decomposition and Teager energy operator techniques on EEG data. The efficacy of these techniques for non-stationary and non-linear data has already been demonstrated, which therefore lend themselves well to EEG signals, which are also non-stationary and non-linear in nature. The method described in this paper decomposed the EEG signals (6 EEG signals per task per subject, for a total of 5 tasks over multiple trials) into their constituent oscillatory modes, called intrinsic mode functions, and separated out the trend from the signal. Teager energy operator was used to calculate the average energy of both the detrended signal and the trend. The average energy was used to construct separate feature vectors with a small number of parameters for the detrended signal and the trend. Based on these parameters, one-versus-one classification of mental tasks was performed using Linear Discriminant Analysis. Using both feature vectors, an average correct classification rate of more than 85% was achieved, demonstrating the effectiveness of the method used. Furthermore, this method used all the intrinsic mode functions, as opposed to similar studies, demonstrating that the trend of the EEG signal also contains important discriminatory information.