The　separation　of　electrooculogram　(EOG)　and　electroencephalogram　(EEG)　is　a　potential　problem　in　brain-computer　interface　(BCI).　Especially,　it　is　necessary　to　accurately　remove　EOG,　as　a　disturbance,　from　the　measured　EEG　in　brain　disease　diagnosis,　EEG-based　rehabilitation　systems,　etc.　Due　to　the　interaction　between　the　eye　and　periocular　musculature,　a　multipoint　spike　is　often　produced　in　EEG　for　each　ocular　activity.　Masking-aided　minimum　arclength　empirical　mode　decomposition　(MAMA-EMD)　was　developed　to　robustly　decompose　time　series　with　impulse-like　noise.　However,　the　decomposition　performance　of　MAMA-EMD　was　limited　in　the　case　of　one　impulse　with　multiple　contiguous　spike　points.　In　this　paper,　MAMA-EMD　was　improved　(called　IMAMA-EMD)　by　supplementing　the　minimum　arclength　criterion,　and　it　was　combined　with　kernel　independent　component　analysis　(KICA),　yielding　an　automatic　EOG　artifact　removal　method,　denoted　as　KIIMME.　The　multi-channel　contaminated　EEG　signals　were　separated　into　several　independent　components　(ICs)　by　KICA.　Then,　IMAMA-EMD　was　applied　to　the　EOG-related　ICs　decomposition　to　generate　a　set　of　inherent　mode　functions　(IMFs),　the　low　frequency　ones,　which　have　higher　correlation　with　EOG　components,　were　removed,　and　the　others　were　employed　to　construct　＇clean＇　EEG.　The　proposed　KIIMME　was　evaluated　and　compared　with　other　methods　on　semisimulated　and　real　EEG　data.　Experimental　results　demonstrated　that　IMAMA-EMD　effectively　eliminated　the　influence　of　multipoint　spike　on　sifting　process,　and　KIIMME　improved　the　removal　accuracy　of　EOG　artifacts　from　EEG　while　retaining　more　useful　neural　data.　This　improvement　is　of　great　significance　to　research　on　brain　science　as　well　as　BCI.　(C)　2021　Nalecz　Institute　of　Biocybernetics　and　Biomedical　Engineering　of　the　Polish　Academy　of　Sciences.　Published　by　Elsevier　B.V.　All　rights　reserved.