In　recent　years,　more　and　more　researchers　are　devoting　themselves　to　the　studies　about　authentication　based　on　biomarkers.　Among　a　wide　variety　of　biomarkers,　code-modulated　visual　evoked　potential　(c-VEP)　has　attracted　increasing　attention　due　to　its　significant　role　in　the　field　of　brain-computer　interface.　In　this　study,　we　designed　a　mild-burdened　cognitive　task　(MBCT),　which　can　check　whether　participants　focus　their　attention　on　the　visual　stimuli　that　evoke　c-VEP.　Furthermore,　we　investigated　the　authentication　based　on　the　c-VEP　evoked　in　the　cognitive　task　by　introducing　a　deep　learning　method.　Seventeen　participants　were　recruited　to　take　part　in　the　MBCT　experiments　including　two　sessions,　which　were　carried　out　on　two　different　days.　The　c-VEP　signals　from　the　first　session　were　extracted　to　train　the　authentication　deep　models.　The　c-VEP　data　of　the　second　session　were　used　to　verify　the　models.　It　achieved　a　desirable　performance,　with　the　average　accuracy　and　F1　score,　respectively,　of　0.92　and　0.89.　These　results　show　that　c-VEP　carries　individual　discriminative　characteristics　and　it　is　feasible　to　develop　a　practical　authentication　system　based　on　c-VEP.