Discriminating mental tasks using EEG represented by AR models

EEG signals are modeled using single-channel and multi-channel autoregressive (AR) techniques. The coefficients of these models are used to classify EEG data into one of two classes corresponding to the mental task the subjects are performing. A neural network is trained to perform the classification. When applying a trained network to test data, the authors find that the multivariate AR representation performs slightly better, resulting in an average classification accuracy of about 91%