Emotion　recognition　is　a　highly　noteworthy　and　challenging　work　in　both　cognitive　science　and　affective　computing.　Currently,　neurobiology　studies　have　revealed　the　partially　synchronous　oscillating　phenomenon　within　brain,　which　needs　to　be　analyzed　from　oscillatory　synchronization.　This　combination　of　oscillations　and　synchronism　is　worthy　of　further　exploration　to　achieve　inspiring　learning　of　the　emotion　recognition　models.　In　this　paper,　we　propose　a　novel　approach　of　valence　and　arousal-based　emotion　recognition　using　EEG　data.　First,　we　construct　the　emotional　oscillatory　brain　network　(EOBN)　inspired　by　the　partially　synchronous　oscillating　phenomenon　for　emotional　valence　and　arousal.　And　then,　a　coefficient　of　variation　and　Welch＇s　t-test　based　feature　selection　method　is　used　to　identify　the　core　pattern　(cEOBN)　within　EOBN　for　different　emotional　dimensions.　Finally,　an　emotional　recognition　model　(ERM)　is　built　by　combining　cEOBN-inspired　information　obtained　in　the　above　process　and　different　classifiers.　The　proposed　approach　can　combine　oscillation　and　synchronization　characteristics　of　multi-channel　EEG　signals　for　recognizing　different　emotional　states　under　the　valence　and　arousal　dimensions.　The　cEOBN-based　inspired　information　can　effectively　reduce　the　dimensionality　of　the　data.　The　experimental　results　show　that　the　previous　method　can　be　used　to　detect　affective　state　at　a　reasonable　level　of　accuracy.