Detection and Classification of Mu Rhythm using Phase Synchronization for a Brain Computer Interface

Abstract

Phase synchronization in a brain computer interface based on Mu rhythm is evaluated by means of phase lag index and weighted phase lag index. In order to detect and classify the important features reflected in brain signals during execution of mental tasks (imagination of left and right hand movement), the proposed methods are implemented on two datasets. The classification is performed using linear discriminant classifier, quadratic discriminant classifier, Mahalanobis distance classifier, k nearest neighbor and support vector machine. Classification accuracies up to 74% and 61% for phase lag index and weighted phase lag index were achieved. The results indicate that phase synchronization measures are relevant for classifying mental tasks recorded in the active state and the relaxation state from additional motor area and from the sensorimotor area. Phase lag index and weighted phase lag index methods are easy to implement, efficient, provide relevant features for the classification and can be used as an offline methods for motor imagery paradigms.