The　breakthrough　of　electroencephalogram　(EEG)　signal　classification　of　brain　computer　interface　(BCI)　will　set　off　another　technological　revolution　of　human　computer　interaction　technology.　Because　the　collected　EEG　is　a　type　of　nonstationary　signal　with　strong　randomness,　effective　feature　extraction　and　data　mining　techniques　are　urgently　required　for　EEG　classification　of　BCI.　In　this　paper,　the　new　bionic　whale　optimization　algorithms　(WOA)　are　proposed　to　promote　the　improved　extreme　learning　machine　(ELM)　algorithms　for　EEG　classification　of　BCI.　Two　improved　WOA-ELM　algorithms　are　designed　to　compensate　for　the　deficiency　of　random　weight　initialization　for　basic　ELM.　Firstly,　the　top　several　best　individuals　are　selected　and　voted　to　make　decisions　to　avoid　misjudgment　on　the　best　individual.　Secondly,　the　initial　connection　weights　and　bias　between　the　input　layer　nodes　and　hidden　layer　nodes　are　optimized　by　WOA　through　bubble-net　attacking　strategy　(BNAS)　and　shrinking　encircling　mechanism　(SEM),　and　different　regularization　mechanisms　are　introduced　in　different　layers　to　generate　appropriate　sparse　weight　matrix　to　promote　the　generalization　performance　of　the　algorithm.As　shown　in　the　contrast　results,　the　average　accuracy　of　the　proposed　method　can　reach　93.67%,　which　is　better　than　other　methods　on　BCI　dataset.