The　Common　Spatial　Pattern　(CSP)　algorithm　is　known　to　be　effective　in　extracting　discriminative　features　from　Motor　Imagery　electroencephalograms　(MI-EEG).　However,　its　performance　depends　on　the　frequency　bands　that　relate　to　brain　activities　associated　with　MI　tasks.　To　achieve　an　accurate　classification,　several　methods　have　been　proposed　to　determine　such　a　set　of　frequency　bands.　However,　the　existing　methods　cannot　find　the　multiple　subject-specific　frequency　bands　adaptively.　Based　on　the　Orthogonal　Empirical　Mode　Decomposition　(OEMD),　FIR　filter　and　CSP　algorithm,　a　novel　feature　extraction　method　called　OEFCSP　is　proposed　to　effectively　perform　the　autonomous　extraction　and　selection　of　key　individual　spatial　discriminative　CSP　features.　A　channel　selection　algorithm　is　applied　to　the　band-pass　filtered　EEG　signals　to　reduce　the　number　of　channels.　Then,　each　remaining　channel　of　the　EEG　signal　is　adaptively　decomposed　into　multiple　orthogonal　Intrinsic　Mode　Functions　(IMFs)　by　OEMD,　and　each　IMF　is　further　equally　divided　into　multiple　sub-band　signals　by　the　band-pass　filters.　Subsequently,　the　CSP　features　are　extracted　from　each　sub-band　signal　and　a　feature　ranking　algorithm　is　employed　to　reorder　the　CSP　features.　Finally,　a　feature　selection　and　classification　algorithm　is　optimized　to　classify　the　selected　CSP　features.　Experiments　are　conducted　on　a　publicly　available　dataset,　and　the　experimental　results　show　that　OEFCSP　yields　relatively　higher　classification　accuracies　compared　to　the　existing　approaches.