Motor　Imagery　Electroencephalography　(MI-EEG)　has　shown　good　prospects　in　neurorehabilitation,　and　the　entropy-based　nonlinear　dynamic　methods　have　been　successfully　applied　to　feature　extraction　of　MI-EEG.　Especially　based　on　Multiscale　Fuzzy　Entropy　(MFE),　the　fuzzy　entropies　of　the　τ　coarse-grained　sequences　in　τ　scale　are　calculated　and　averaged　to　develop　the　Composite　MFE　(CMFE)　with　more　feature　information.　However,　the　coarse-grained　process　fails　to　match　the　nonstationary　characteristic　of　MI-EEG　by　a　mean　filtering　algorithm.　In　this　paper,　CMFE　is　improved　by　assigning　the　different　weight　factors　to　the　different　sample　points　in　the　coarse-grained　process,　i.e.,　using　the　weighted　mean　filters　instead　of　the　original　mean　filters,　which　is　conductive　to　signal　filtering　and　feature　extraction,　and　the　resulting　personalized　Weighted　CMFE　(WCMFE)　is　more　suitable　to　represent　the　nonstationary　MI-EEG　for　different　subjects.　All　the　WCMFEs　of　multi-channel　MI-EEG　are　fused　in　serial　to　construct　the　feature　vector,　which　is　evaluated　by　a　back-propagation　neural　network.　Based　on　a　public　dataset,　extensive　experiments　are　conducted,　yielding　a　relatively　higher　classification　accuracy　by　WCMFE,　and　the　statistical　significance　is　examined　by　two-sample　t-test.　The　results　suggest　that　WCMFE　is　superior　to　the　other　entropy-based　and　traditional　feature　extraction　methods.