Robotic-assisted　rehabilitation　system　based　on　Brain-Computer　Interface　(BCI)　is　an　applicable　solution　for　stroke　survivors　with　a　poorly　functioning　hemiparetic　arm.　The　key　technique　for　rehabilitation　system　is　the　feature　extraction　of　Motor　Imagery　Electroencephalography　(MI-EEG),　which　is　a　nonlinear　time-varying　and　nonstationary　signal　with　remarkable　time-frequency　characteristic.　Though　a　few　people　have　made　efforts　to　explore　the　nonlinear　nature　from　the　perspective　of　manifold　learning,　they　hardly　take　into　full　account　both　time-frequency　feature　and　nonlinear　nature.　In　this　paper,　a　novel　feature　extraction　method　is　proposed　based　on　the　Locally　Linear　Embedding　(LLE)　algorithm　and　DWT.　The　multiscale　multiresolution　analysis　is　implemented　for　MI-EEG　by　DWT.　LLE　is　applied　to　the　approximation　components　to　extract　the　nonlinear　features,　and　the　statistics　of　the　detail　components　are　calculated　to　obtain　the　time-frequency　features.　Then,　the　two　features　are　combined　serially.　A　backpropagation　neural　network　is　optimized　by　genetic　algorithm　and　employed　as　a　classifier　to　evaluate　the　effectiveness　of　the　proposed　method.　The　experiment　results　of　10-fold　cross　validation　on　a　public　BCI　Competition　dataset　show　that　the　nonlinear　features　visually　display　obvious　clustering　distribution　and　the　fused　features　improve　the　classification　accuracy　and　stability.　This　paper　successfully　achieves　application　of　manifold　learning　in　BCI.