Classification of Motor Imagery Hand Movement Directions from EEG extracted Phase Locking Value features for Brain Computer Interfaces

Abstract

Brain-Computer Interface (BCI) systems translate the users intentions coded by brain activity measures into actions through a control signal without using activity of any muscles or peripheral nerves. Usually, in Electroencephalography (EEG) based BCI experiment protocols, different mental tasks are performed to elicit unique brain signal responses, which are recognized by signal processing and machine learning methods. The work presented in this paper extracts the EEG phase synchrony feature called Phase Lock Value (PLV) to decode Motor Imagery (MI) of center-out hand movement in right and left directions. At first, PLV features of all the EEG channel pairs are extracted to detect the level of synchronization corresponding to the directional hand movements. The most significant channel pairs selected from direction-specific EEG signals corresponding to the imagined hand movement showed characteristic changes in PLV features. Mean Percentage Difference of PLV features are calculated and compared in different frequency bands to identify the most discriminative frequency band for the hand movement classification. The extracted PLV features offer 5.34% improvement in classification accuracy for the 7 best performing subjects compared to the relevant method in literature.