Classification of Motor Imagery EEG Based on Phase Synchronization

Phase synchrony between electroencephalogram(EEG) signals is considered as mechanism of brain function and regional integration.So Phase synchrony will be applied to classification of motor imagery.In suitable time window,selecting the electrodes of C3、C4 and central regions to match,Hilbert transform signal processing method was used to extract the degree of phase synchronization between two EEG signals by calculating the so-called phase locking value(PLV).The support vector machine(SVM) is used for classification of the motor imagery by a feature selection algorithm.It's shown that the satisfactory results were obtained with single-trial accuracies of 92.5%.