In　order　to　improve　the　accuracy　of　brain　signal　processing　and　accelerate　speed　meanwhile,　we　present　an　optimal　and　intelligent　method　for　large　dataset　classification　application　in　this　paper.　Optimized　Extreme　Learning　Machine　(OELM)　is　introduced　in　ElectroCorticoGram　(ECoG)　feature　classification　of　motor　imaginary-based　brain-computer　interface　(BCI)　system,　with　common　spatial　pattern　(CSP)　to　extract　the　feature.　When　comparing　it　with　other　conventional　classification　methods　like　SVM　and　ELM,　we　exploit　several　metrics　to　evaluate　the　performance　of　all　the　adopted　methods　objectively.　The　accuracy　of　the　proposed　BCI　system　approaches　approximately　92.31%　when　classifying　ECoG　epochs　into　left　pinky　or　tongue　movement,　while　the　highest　accuracy　obtained　by　other　methods　is　no　more　than　81%,　which　substantiates　that　OELM　is　more　efficient　than　SVM,　ELM,　etc.　Moreover,　the　simulation　results　also　demonstrate　that　OELM　will　significantly　improve　the　performance　with　p　value　being　far　less　than　0.001.　Hence,　the　proposed　OELM　is　satisfactory　in　addressing　ECoG　signal.　©　2020　Xinman　Zhang　et　al.