In　order　to　adaptively　extract　the　subject-based　time-frequency　features　of　motor　imagery　EEG　(MI-EEG)　and　make　full　use　of　the　sequential　information　hidden　in　MI-EEG　features,　a　Long　Short-Term　Memory　(LSTM)　based　Recurrent　Neural　Network　(RNN)　is　integrated　with　Optimal　Wavelet　Packet　Transform　(OWPT)　to　yield　a　novel　recognition　method,　denoted　as　OWLR.　Firstly,　OWPT　is　applied　to　each　channel　of　MI-EEG,　and　the　improved　distance　criterion　is　used　to　find　the　optimal　wavelet　packet　subspaces,　whose　coefficients　are　further　selected　as　the　time-frequency　features　of　MI-EEG.　Finally,　a　LSTM　based　RNN　is　used　for　classifying　MI-EEG　features.　Experiments　are　conducted　on　a　publicly　available　dataset,　and　the　5-fold　cross　validation　experimental　results　show　that　OWLR　yields　relatively　higher　classification　accuracies　compared　to　the　existing　approaches.　This　is　helpful　for　the　future　research　and　application　of　RNN　in　processing　of　MI-EEG.