When　performing　studies　on　brain　computer　interface　based　rehabilitation　problems,　researchers　frequently　encounter　difficulty　due　to　the　curse　of　dimensionality　and　the　nonlinear　nature　of　Motor　Imagery　Electroencephalography　(MI-EEG).　Though　many　approaches　have　been　proposed　recently　to　address　the　feature　extraction　problem　and　have　shown　surprising　performance,　unfortunately,　most　of　them　are　non-parametric　or　linear　dimension　reduction　techniques,　which　are　limited　in　utility　for　out　of-sample　extension　for　MI-EEG　classification.　To　address　the　problem　and　obtain　accurate　MI-EEG　features,　a　new　unsupervised　nonlinear　dimensionality　reduction　technique　termed　parametric　t-Distributed　Stochastic　Neighbor　Embedding　(P.　t-SNE)　is　employed　to　extract　the　nonlinear　features　from　MI-EEG.　Considering　that　MI-EEG　is　a　kind　of　non-stationary　signal　with　remarkable　time-frequency　rhythmic　distribution　characteristics,　Discrete　Wavelet　Transform　(DWT)　is　used　to　extract　the　time　frequency　features　of　MI-EEG.　Furthermore,　P.　t-SNE　is　applied　to　selected　wavelet　components　to　get　the　nonlinear　features.　They　are　then　combined　serially　to　construct　the　feature　vector.　Experiments　are　conducted　on　a　publicly　available　dataset,　and　the　experimental　results　show　that　the　nonlinear　features　have　great　visualization　performance　with　obvious　clustering　distribution,　and　the　feature　extraction　method　indicates　excellent　classification　performance　as　evaluated　by　a　support　vector　machine　classifier.　This　paper　suggests　a　manifold　based　technique　for　further　analysis　and　classification　research　of　MI　EEG.　(C)　2016　Elsevier　B.V.　All　rights　reserved.