With　the　rapid　development　of　computer　technology,　pervasive　computing　and　wearable　devices,　EEG-based　emotion　recognition　has　gradually　attracted　much　attention　in　affecting　computing　(AC)　domain.　In　this　paper,　we　propose　an　approach　of　emotion　recognition　using　EEG　signals　based　on　the　weighted　fusion　of　multiple　base　classifiers.　These　base　classifiers　based　on　SVM　are　constructed　using　a　channel　division　mechanism　according　to　the　neuropsychological　theory　that　different　brain　areas　are　differ　in　processing　intensity　of　emotional　information.　The　outputs　of　channel　base　classifiers　are　integrated　by　a　weighted　fusion　strategy　which　is　based　on　the　confidence　estimation　on　each　emotional　label　by　each　base　classifier.　The　evaluation　on　the　DEAP　dataset　shows　that　our　proposed　multiple　classifiers　fusion　method　outperforms　individual　channel　base　classifiers　and　the　feature　fusion　method　for　EEG-based　emotion　recognition.