In　recent　years,　with　the　rapid　development　of　brain　science　and　information　science,　affective　Brain　Computer　Interfaces　(aBCI)　have　become　a　hot　topic.　EEG　signals　which　record　the　electrical　activity　of　neurons　in　the　brain　can　provide　direct　evidence　for　emotional　states.　Compared　with　end-to-end　deep　learning　methods,　expert　knowledge　based　emotion　recognition　methods,　using　hand-crafted　features,　are　computationally　cheaper　and　can　have　better　generalization　ability　and　interpretability.　Due　to　the　effort　of　balancing　the　energy　spectrum　of　EEG　signals,　differential　entropy　features　have　been　proved　a　powerful　tool　for　emotion　recognition.　However,　differential　entropy　of　EEG　signals　still　exhibits　a　decrease　trend　with　increasing　frequency,　resulting　in　a　low　representation　of　the　effect　of　high-frequency　components　in　emotion　recognition　and　do　not　perfectly　solve　the　problem　caused　by　the　1/f　power　law　of　EEG　signals.　In　this　paper,　a　decibel　(dB)　feature　is　extracted　by　introducing　baseline　normalization　of　EEG　power　spectral　density　(PSD)　,　which　balances　the　effect　of　different　rhythmic　components　better.　On　this　basis,　for　the　features　are　smoothed　and　a　sparse　graph　regularized　extreme　learning　machine　(SGELM)　is　used　to　compare　the　dB　feature　with　DE　in　emotion　recognition.　The　proposed　method　is　validated　on　DEAP　data　set,　and　the　average　classification　accuracy　increased　by　11.33%　over　those　of　DE　features　with　wild　feature　smoothing,　and　by　2.96%　with　high　feature　smoothness.　The　baseline　normalized　DE　feature　not　only　provide　a　new　option　for　low-cost　affective　brain-computer　interfaces　can　but　also　be　used　as　an　alternative　input　feature　for　deep　learning　models.　©　2023　IEEE.