Electroencephalography　(EEG)　is　considered　the　output　of　a　brain　and　it　is　a　bioelectrical　signal　with　multiscale　and　nonlinear　properties.　Motor　Imagery　EEG　(MI-EEG)　not　only　has　a　close　correlation　with　the　human　imagination　and　movement　intention　but　also　contains　a　large　amount　of　physiological　or　disease　information.　As　a　result,　it　has　been　fully　studied　in　the　field　of　rehabilitation.　To　correctly　interpret　and　accurately　extract　the　features　of　MI-EEG　signals,　many　nonlinear　dynamic　methods　based　on　entropy,　such　as　Approximate　Entropy　(ApEn),　Sample　Entropy　(SampEn),　Fuzzy　Entropy　(FE),　and　Permutation　Entropy　(PE),　have　been　proposed　and　exploited　continuously　in　recent　years.　However,　these　entropy-based　methods　can　only　measure　the　complexity　of　MI-EEG　based　on　a　single　scale　and　therefore　fail　to　account　for　the　multiscale　property　inherent　in　MI-EEG.　To　solve　this　problem,　Multiscale　Sample　Entropy　(MSE),　Multiscale　Permutation　Entropy　(MPE),　and　Multiscale　Fuzzy　Entropy　(MFE)　are　developed　by　introducing　scale　factor.　However,　MFE　has　not　been　widely　used　in　analysis　of　MI-EEG,　and　the　same　parameter　values　are　employed　when　the　MFE　method　is　used　to　calculate　the　fuzzy　entropy　values　on　multiple　scales.　Actually,　each　coarse-grained　MI-EEG　carries　the　characteristic　information　of　the　original　signal　on　different　scale　factors.　It　is　necessary　to　optimize　MFE　parameters　to　discover　more　feature　information.　In　this　paper,　the　parameters　of　MFE　are　optimized　independently　for　each　scale　factor,　and　the　improved　MFE　(IMFE)　is　applied　to　the　feature　extraction　of　MI-EEG.　Based　on　the　event-related　desynchronization　(ERD)/event-related　synchronization　(ERS)　phenomenon,　IMFE　features　from　multi　channels　are　fused　organically　to　construct　the　feature　vector.　Experiments　are　conducted　on　a　public　dataset　by　using　Support　Vector　Machine　(SVM)　as　a　classifier.　The　experiment　results　of　10-fold　cross-validation　show　that　the　proposed　method　yields　relatively　high　classification　accuracy　compared　with　other　entropy-based　and　classical　time-frequency-space　feature　extraction　methods.　The　t-test　is　used　to　prove　the　correctness　of　the　improved　MFE.