Aiming at the feature extraction issue of four class motor imagerytask,this paper proposes an EEG signal feature extraction method based on intrinsic timescale decomposition (ITD) and phase synchronization analysis.The fourclass motorimagery datasets from the BCI Competition III and BCI Competition IV are adopted.Firstly,this method selects five channel motorimagery EEG (electroencephalogram) signals,calculates the phase locking value(PLV) among the channels according to the phase synchronization,and uses the PLV as a kind of feature. Then,ITD is used to decompose the five channel motorimagery EEG signals and extract the energy feature of the first layer proper rotation component(PRC), which is combined with the PLV feature to obtain the fifteendimensional feature vector. Finally, support vector machine (SVM) is used for classification recognition. The average recognition rate and Kappa coefficient for 12 subjects reach 9164% and 0887,respectively. The results show that this method can effectively extract the feature of EEG signals and improve the classification accuracy of fourclass motor imagery task.