In　order　to　get　high　classification　accuracy,　feature　combination　is　commonly　used　in　analysis　of　motor　imagery　electroencephalography　EEG　signal,　including　the　nonlinear　analysis　and　traditional　time-frequency　analysis.　In　this　paper,　Sample　entropy(SampEn)　was　computed　and　represented　as　the　nonlinear　feature　of　motor　imagery　EEG　signal　for　it　can　quantify　the　probability　of　new　information　appeared　in　time　series.　In　addition,　orthogonal　empirical　mode　decomposition　(OEMD)　was　also　employed　to　extract　the　average　energy　of　selected　intrinsic　mode　functions(IMF)　as　the　time-frequency　feature　for　motor　imagery　EEG　signal.　Based　on　a　public　dataset,　many　experiments　were　conducted.　Slide　window　was　used　to　select　the　best　time　period　for　a　better　performance　in　feature　extraction,　and　cross　validation　of　10　folds　was　applied　in　all　the　classification　procedure.　The　highest　recognition　rate　using　SampEn　and　OEMD　is　respectively　86.07%　and　83.21%　classified　by　incremental　support　vector　machine　(ISVM)　respectively.　However,　the　highest　classification　rate　of　combined　features　is　86.79%　by　using　ISVM　which　is　a　little　higher　than　that　of　SampEn.　Big　linear　correlation　between　SampEn　and　energy　of　IMF　explains　why　the　classification　accuracy　by　combining　the　two　types　of　features　is　not　as　high　as　expected.