Motor　imagery-EEG　based　on　brain　computer　interface　(BCI)　provide　a　promising　way　to　establish　pathways　for　neural　center　and　external　equipment,　which　has　been　widely　used　in　the　fields　of　neurological　rehabilitation　and　robot　control.　During　this　process,　the　performance　of　BCI　depends　on　the　accuracy　of　decoding　for　the　motor　intention.　Due　to　the　high　efficiency　and　simplicity,　spatial　filtering　is　often　used　to　extract　the　amplitude　feature　for　MI　classification.　However,　changes　of　physiological　condition　for　subjects　would　cause　high　variability　of　EEG　signal　within　the　trials/sessions,　which　brought　huge　challenge　of　feature　extraction　and　classification.　The　state　of　phase　synchronization　for　EEG　is　an　important　information　to　evaluate　the　motor　intention,　which　is　rarely　combine　with　amplitude　feature　to　realize　the　pattern　recognition.　Therefore,　in　this　study,　we　propose　a　model　integrating　phase　and　amplitude　information　for　binary　classification　in　MI　task.　Firstly,　we　adopt　the　phase-locked　values　to　calculate　the　time　section　with　degree　of　phase　synchronization　that　contains　the　maximum　discriminant　information.　Then,　common　spatial　pattern　was　performed　to　extract　amplitude　feature　for　α　and　β　bands　integrating　the　distribution　of　phase　information.　Next,　SVM　was　utilized　to　classify　feature　vectors　and　realize　the　binary　MI　decoding.　Five　subjects　recruited　to　participate　in　the　experiment　and　results　show　that　the　information　of　phase　synchronization　significantly　improves　the　classification　performance　of　spatial　filter.　©　2021　ACM.