Motor　Imagery　EEG　or　ECoG　is　the　most　popular　driving　signal　in　brain　computer　interface　based　rehabilitation　system.　Empirical　Mode　Decomposition　(EMD)　can　be　employed　in　feature　extraction,　which　only　a　single　scale　IMF　is　considered　by　using　Phase　Locking　Value　(PLV),　leading　to　the　loss　of　phase　information.　In　this　paper,　a　Multi-period　Multivariate　Multi-scale　PLV　(M,MIMPLV)　is　proposed　based　on　Noise-Assisted　Multivariate　EMD　(NAMEMD).　The　selected　multi-channel　MI-ECoG　are　decomposed　simultaneously　by　NAMEMD　to　obtain　the　multivariate　multi-scale　IMFs,　and　their　length　are　divided　into　many　periods.　Then　the　PLV　of　pair-wise　IMFs　at　the　same　scale　are　calculated　in　each　time　period　for　any　two-channel　MI-ECoG　signals.　The　resulted　MIMIMPLV　are　constructed　as　phase　features.　Furthermore,　the　phase　features　generated　by　MMIMPLV　and　the　spatial　features　extracted　by　Common　Spatial　Subspace　Decomposition　(CSSD)　algorithm　are　fused　in　series,　yielding　a　new　feature　extraction　method,　denoted　as　MMMPC.　Experiments　were　conducted　on　a　public　database,　and　the　Support　Vector　Machine　(SVM)　is　used　to　classify　the　combined　features.　The　experiment　results　of　9-fold　Cross　Validation　(CV)　show　that　the　proposed　method　yields　relative　higher　classification　accuracy　and　better　stability　compared　with　the　other　synchronization　methods　and　classical　feature　extraction　methods.