Abstract-We propose an off-line analysis method in order to discriminate between motor imagery tasks manipulated in a brain computer interface system. A measure of large-scale synchronization based on phase locking value is established. The results indicate that it can take advantage of the phase synchrony between scalp-recorded EEG activity in the supplementary motor area and in sezorimotor area, computing the differences between the active and the relaxation states. Phase locking value features are more discriminative in β rhythm than in µ rhythm. The proposed method is simple, computationally efficient and proves good results on EEG Motor Movement/Imagery Dataset available from PhysioNet research resource for physiologic signals.

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An offline analysis method is proposed for a brain computer interface paradigm. Changes that appear in brain during the motor tasks should be reflected in the EEG signals. The sequences of EEG data are modeled by autoregressive (AR) processes. Based on Itakura distance (ID), the differences that occur during mental tasks (left and right hand movement imagination) versus relaxation period are measured. After applying statistical tests, channels selection is performed. The data contained in the chosen channels are classified with linear discriminant classifier (LDA), quadratic discriminant classifier (QDA) and Mahalanobis distance classifier (MD). The advantage of channels selection based on ID is that the picked channels contain relevant features. The effectiveness of the method is sustained by the classification rates obtained.