An　integrated　method　was　proposed　to　achieve　the　objective　accuracy　of　emotion　recognition　from　the　electroencephalograph　(EEG)　signal.　The　EEG　signals　from　DEAP　data　set　were　decomposed　into　multiple　intrinsic　mode　functions　(IMFs)　with　the　empirical　mode　decomposition　(EMD)　method.　After　that,　the　power　spectrum　density　was　extracted　as　the　EEG　feature　from　the　IMFs　with　different　time　windows.　The　emotion　estimated　scales　of　the　subjects　were　mapped　in　the　Valence-Arousal　emotion　model　to　be　clustered　into　4　classes.　Gaussian　kernel　function　support　vector　machine　(SVM)　was　adopted　to　classify　the　emotion　states.　＂One　versus　one＂　model　was　employed　within　the　SVM　to　deal　with　the　multi-classification　problem.　Results　show　that　the　highest　accuracy　of　the　emotion　classification　obtained　by　the　Gaussian　kernel　function　SVM　acquires　is　90.9%　(with　subject　22),　and　the　mean　accuracy　is　68.31%.　The　results　explain　that　the　method　can　recognize　different　emotion　from　EEG　signal　effectively,　and　that　different　participants　have　different　emotion　experiences　with　the　same　stimulus,　and　that　the　EEG　features　from　high　frequency　IMFs　achieve　higher　emotion　classification　accuracy　than　that　from　low　frequency　IMFs.　©　2018,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.