Motor　imagery　EEG　(MI-EEG),　which　reflects　one＇s　active　movement　intention,　has　attracted　increasing　attention　in　rehabilitation　therapy,　and　accurate　and　fast　feature　extraction　is　the　key　problem　to　successful　applications.　Based　on　wavelet　packet　decomposition　(WPD)　and　SE-isomap,　an　adaptive　feature　extraction　method　is　proposed　in　this　paper.　The　MI-EEG　is　preprocessed　to　determine　a　more　effective　time　interval　through　average　power　spectrum　analysis.　WPD　is　then　applied　to　the　selected　segment　of　MI-EEG,　and　the　subject-based　optimal　wavelet　packets　(OWPs)　with　top　mean　variance　difference　are　obtained　autonomously.　The　OWP　coefficients　are　further　used　to　calculate　the　time-frequency　features　statistically　and　acquire　the　nonlinear　manifold　structure　features,　as　well　as　the　explicit　nonlinear　mapping,　through　SE-isomap.　The　hybrid　features　are　obtained　in　a　serial　fusion　way　and　evaluated　by　a　k-nearest　neighbor　(KNN)　classifier.　The　extensive　experiments　are　conducted　on　a　publicly　available　dataset,　and　the　experiment　results　of　10-fold　cross-validation　show　that　the　proposed　method　yields　relatively　higher　classification　accuracy　and　computation　efficiency　simultaneously　compared　with　the　commonly-used　linear　and　nonlinear　approaches.