Due　to　the　nonlinear　and　high-dimensional　characteristics　of　motor　imagery　electroencephalography　(MI-EEG),　it　can　be　challenging　to　get　high　online　accuracy.　As　a　nonlinear　dimension　reduction　method,　landmark　maximum　variance　unfolding　(L-MVU)　can　completely　retain　the　nonlinear　features　of　MI-EEG.　However,　L-MVU　still　requires　considerable　computation　costs　for　out-of-sample　data.　An　incremental　version　of　L-MVU　(denoted　as　IL-MVU)　is　proposed　in　this　paper.　The　low-dimensional　representation　of　the　training　data　is　generated　by　L-MVU.　For　each　out-of-sample　data,　its　nearest　neighbors　will　be　found　in　the　high-dimensional　training　samples　and　the　corresponding　reconstruction　weight　matrix　be　calculated　to　generate　its　low-dimensional　representation　as　well.　IL-MVU　is　further　combined　with　the　dual-tree　complex　wavelet　transform　(DTCWT),　which　develops　a　hybrid　feature　extraction　method　(named　as　IL-MD).　IL-MVU　is　applied　to　extract　the　nonlinear　features　of　the　specific　subband　signals,　which　are　reconstructed　by　DTCWT　and　have　the　obvious　event-related　synchronization/event-related　desynchronization　phenomenon.　The　average　energy　features　of　and　waves　are　calculated　simultaneously.　The　two　types　of　features　are　fused　and　are　evaluated　by　a　linear　discriminant　analysis　classifier.　Based　on　the　two　public　datasets　with　12　subjects,　extensive　experiments　were　conducted.　The　average　recognition　accuracies　of　10-fold　cross-validation　are　92.50%　on　Dataset　3b　and　88.13%　on　Dataset　2b,　and　they　gain　at　least　1.43%　and　3.45%　improvement,　respectively,　compared　to　existing　methods.　The　experimental　results　show　that　IL-MD　can　extract　more　accurate　features　with　relatively　lower　consumption　cost,　and　it　also　has　better　feature　visualization　and　self-adaptive　characteristics　to　subjects.　The　t-test　results　and　Kappa　values　suggest　the　proposed　feature　extraction　method　reaches　statistical　significance　and　has　high　consistency　in　classification.