This　paper　presents　a　multimodal　fusion　approach　using　kernel-based　Extreme　Learning　Machine　(ELM)　for　video　emotion　recognition　by　combing　video　content　and　electroencephalogram　(EEG)　signals.　Firstly,　several　audio-based　features　and　visual-based　features　are　extracted　from　video　clips　and　EEG　features　are　obtained　by　using　Wavelet　Packet　Decomposition　(WPD).　Secondly,　video　features　are　selected　using　Double　Input　Symmetrical　Relevance　(DISR)　and　EEG　features　are　selected　by　Decision　Tree　(DT).　Thirdly,　multimodal　fusion　using　kernel-based　ELM　is　adopted　for　classification　by　combing　video　and　EEG　features　at　decision-level.　In　order　to　test　the　validity　of　the　proposed　method,　we　design　and　conduct　the　EEG　experiment　to　collect　data　that　consisted　of　video　clips　and　EEG　signals　of　subjects.　We　compare　our　method　separately　with　single　mode　methods　of　using　video　content　only　and　EEG　signals　only　on　classification　accuracy.　The　experimental　results　show　that　the　proposed　fusion　method　produces　better　classification　performance　than　those　of　the　video　emotion　recognition　methods　which　use　either　video　content　or　EEG　signals　alone.