Motor　Imagerary　EEG　(MI-EEG)　signals　have　non-stationary　and　nonlinear　characteristics,　it　is　necessary　to　extract　and　fuse　the　features　in　multi-domain.　Local　Mean　Decomposition　(LMD)　algorithm　has　been　applied　to　feature　extraction　of　MI-EEG.　However,　it　is　limited　due　to　mode　mixing　and　endpoint　effect.　In　this　paper,　the　traditional　LMD　algorithm　is　improved　for　feature　extraction　and　fusion　in　multi-domain.　We　use　pairwise　complementary　white　noise　and　extremum　extension　to　improve　LMD　algorithm,　and　denoted　as　CEELMD.　Then,　use　CEELMD　to　decompose　each　channel　of　EEG　signal　to　obtain　multiple　product　function　(PF)　components.　According　to　the　frequency　band　of　motor　imagery　to　choose　PF　components,　and　their　multi-scale　fuzzy　entropies　(MFE)　are　calculated　as　nonlinear　features,　the　CSP　algorithm　is　used　to　extract　the　spatial　features.　Furthermore,　the　features　are　fused　in　serial　and　evaluated　by　the　random　forest　algorithm.　In　this　paper,　the　experiments　are　conducted　on　the　BCI　IV　2a　competition　data　set　and　the　average　classification　accuracy　reaches　75.27%.　The　experimental　results　show　that　CEELMD　can　effectively　improve　mode　mixing　and　endpoint　effect,　it　is　helpful　to　feature　extraction　for　distinguishing　four　types　of　motor　imagery　EEG　signals.　©　2023　IEEE.