Based　on　local　mean　decomposition　(LMD)　and　canonical　correlation　analysis　(CCA),　an　automatic　removal　method,　denoted　as　LMDC,　was　proposed　to　eliminate　the　serious　impact　of　ocular　artifact　(OA)　from　electroencephalography　(EEG).　Each　recorded　EEG　was　decomposed　into　a　series　of　physically　meaningful　production　function　(PF)　components　adaptively　by　LMD,　and　CCA　was　applied　to　eliminate　the　correlation　among　the　PFs　to　get　the　corresponding　canonical　correlation　variable.　Then,　the　correlation　coefficient　matrix　between　each　EEG　and　multi　electrooculogram　(EOG)　was　computed　to　recognize　the　OA　component　automatically.　The　random　variables　corresponding　with　OA　components　in　the　canonical　correlation　variable　were　set　to　zero,　and　the　others　remain　unchanged　to　obtain　a　new　canonical　correlation　variable.　Finally,　the　inverse　algorithm　of　CCA　was　utilized　to　project　the　new　canonical　correlation　variable　to　the　OA　free　PFs,　and　the　OA　removed　EEG　was　reconstructed.　Experimental　research　was　conducted　on　a　public　brain　computer　interface　(BCI)　completion　database.　Experiment　results　show　that　LMDC　has　better　performance　than　that　of　the　other　related　methods,　and　has　stronger　adaptability　for　multi　subjects　and　types　of　OA.　©　2016,　Beijing　University　of　Technology.　All　right　reserved.