Motor　Imagery　Electroencephalography　(MI-EEG)　plays　an　important　role　in　brain　computer　interface　(BCI)　based　rehabilitation　robot,　and　its　recognition　is　the　key　problem.　The　Discrete　Wavelet　Transform　(DWT)　has　been　applied　to　extract　the　time-frequency　features　of　MI-EEG.　However,　the　existing　EEG　classifiers,　such　as　support　vector　machine　(SVM),　linear　discriminant　analysis　(LDA)　and　BP　network,　did　not　make　full　use　of　the　time　sequence　information　in　time-frequency　features,　the　resulting　recognition　performance　were　not　very　ideal.　In　this　paper,　a　Long　Short-Term　Memory　(LSTM)　based　recurrent　Neural　Network　(RNN)　is　integrated　with　Discrete　Wavelet　Transform　(DWT)　to　yield　a　novel　recognition　method,　denoted　as　DWT-LSTM.　DWT　is　applied　to　analyze　the　each　channel　of　MI-EEG　and　extract　its　effective　wavelet　coefficients,　representing　the　time-frequency　features.　Then　a　LSTM　based　RNN　is　used　as　a　classifier　for　the　patten　recognition　of　observed　MI-EEG　data.　Experiments　are　conducted　on　a　publicly　available　dataset,　and　the　5-fold　cross　validation　experimental　results　show　that　DWT-LSTM　yields　relatively　higher　classification　accuracies　compared　to　the　existing　approaches.　This　is　helpful　for　the　further　research　and　application　of　RNN　in　processing　of　MI-EEG.