A　sample　entropy　(SampEn)-based　emotion　recognition　approach　was　presented.　The　SampEn　results　of　notable　EEG　channels　screened　by　K-S　test　were　fed　to　the　support　vector　machine　(SVM)-weight　classifier　for　training,　after　which　it　was　applied　to　two　emotion　recognition　tasks.　One　is　to　distinguish　positive　and　negative　emotion　with　high　arousal　and　the　other　genitive　emotion　with　different　arousal　status.　Results　showed　that　channels　related　to　emotions　were　mostly　located　on　the　prefrontal　region,　i.e.,　F3,　CP5,　FP2,　FZ,　and　FC2.　And　they　were　applied　to　form　the　input　vectors　of　SVM-weight　classifier.　The　accuracies　of　the　present　algorithm　for　the　two　tasks　were　80.43%　and　79.11%,　respectively　indicated　by　the　leave-one-person-out　validation　procedure,　demonstrating　that　the　present　algorithm　had　a　reasonable　generalization　capability.