Motor　imagery　electroencephalogram　(MI-EEG)　play　an　important　role　in　the　field　of　neurorehabilitation,　and　a　fuzzy　support　vector　machine　(FSVM)　is　one　of　the　most　used　classifiers.　Specifically,　a　fuzzy　c-means　(FCM)　algorithm　was　used　to　membership　calculation　to　deal　with　the　classification　problems　with　outliers　or　noises.　However,　FCM　is　sensitive　to　its　initial　value　and　easily　falls　into　local　optima.The　joint　optimization　of　genetic　algorithm　(GA)　and　FCM　is　proposed　to　enhance　robustness　of　fuzzy　memberships　to　initial　cluster　centers,　yielding　an　improved　FSVM　(GF-FSVM).The　features　of　each　channel　of　MI-EEG　are　extracted　by　the　improved　refined　composite　multivariate　multiscale　fuzzy　entropy　and　fused　to　form　a　feature　vector　for　a　trial.　Then,　GA　is　employed　to　optimize　the　initial　cluster　center　of　FCM,　and　the　fuzzy　membership　degrees　are　calculated　through　an　iterative　process　and　further　applied　to　classify　two-class　MI-EEGs.Extensive　experiments　are　conducted　on　two　publicly　available　datasets,　the　average　recognition　accuracies　achieve　99.89%　and　98.81%　and　the　corresponding　kappa　values　are　0.9978　and　0.9762,　respectively.The　optimized　cluster　centers　of　FCM　via　GA　are　almost　overlapping,　showing　great　stability,　and　GF-FSVM　obtains　higher　classification　accuracies　and　higher　consistency　as　well.