In　recent　years,　individual　recognition　systems　based　on　brain　activity　have　attracted　more　and　more　attention.　In　this　kind　of　system,　visual　evoked　potentials　(VEP)　are　a　good　option　for　biometrics　due　to　their　high　information　transfer　rate,　non-invasiveness,　relatively　low　cost,　and　portability.　This　study　uses　the　code-modulated　visual　evoked　potential(c-VEP)　as　a　biometric　signal　for　individual　identification.　The　study　combined　c-　YEP　with　an　affordable　task　to　ensure　that　subjects＇　attention　was　devoted　to　the　experiment　while　subjects＇　characteristics　were　not　altered　by　the　affordable　of　the　task.　Twelve　subjects　were　recruited　to　participate　in　the　experiment　in　this　study.　Deep　learning　methods　(CNN,　EEGNet)　were　used　on　a　dataset　of　60　electrodes　of　c-　YEP　from　12　subjects　to　achieve　more　than　99%　accuracy　in　individual　identification.　The　identification　accuracy　exceeds　98%　when　only　3　electrodes　are　used.　The　results　indicate　that　the　use　of　c-　YEP　as　a　biosignal　for　individual　recognition　has　promising　applications.　©　2022　IEEE.