Aiming　to　solve　such　problems　as　the　difficulties　in　feature　extraction,　high　computational　complexity　and　low　signal　recognition　rate　when　using　traditional　common　spatial　pattern　(CSP)　algorithm　to　process　electroencephalogram　(EEG)　signals,　an　algorithm　based　on　repealed　bisection　filler　bank　(RB-FBCSP)　and　support　vector　machine　(SVM)　was　proposed.　The　algorithm　is　a　method　used　to　recognize　EEG　signals　of　four　types　of　facial　expressions　(left　smirk,　right　smirk,　frown　brown,　and　raise　brow).　Firstly,　the　collected　facial　expression　EEG　signals　are　filtered　out　by　filter　bank　to　select　the　signals　including　a　wave　and　9　wave;　secondly,　the　four　types　of　expressions　are　regarded　as　upper　facial　expressions　(frown　brown　and　raise　brow)　and　lower　facial　expressions　(left　smirk　and　right　smirk).　CSP　feature　extraction　is　carried　out　in　two　categories,　and　combined　with　SVM　classifier　for　classification;　finally,　the　EEG　signals　of　the　identified　upper　and　lower　facial　expressions　are　repeatedly　subjected　to　CSP　feature　extraction　and　SVM　two-classification,　and　the　four　classifications　of　auxiliary　EEG　signals　can　be　realized.　The　experimental　results　show　that　the　computational　complexity　of　the　proposed　recognition　method　is　significantly　reduced,　the　calculation　time　is　shorter,　and　the　average　classification　accuracy　is　as　high　as　89.　614%.　Compared　to　the　traditional　OVO-CSP,　OVR-CSP　and　wavelet　packet　transform　algorithm　combination,　the　average　recognition　rates　obtained　by　SVM　classification　arc　improved　by　9.　23%,　9.　82%　and　8.　045%,　respectively.　©　2022　Xi＇an　Jiaotong　University.　All　rights　reserved.