This　work　designs　an　adversarial　Bayesian　deep　network　to　solve　the　cognitive　detection　of　pilot　fatigue.　Batch　normalization　and　data　enhancement　are　adopted　in　the　posterior　inference　of　the　proposed　model　parameters　to　effectively　improve　the　generalization　of　neural　networks.　The　generator　is　used　to　enhance　the　brain　power　map　generated　from　three　cognitive　indicators　and　improve　the　accuracy　of　fatigue　state　recognition.　This　work　also　adds　adversarial　noise　in　the　vicinity　of　each　brain　electrode　to　form　an　adversarial　image,　which　further　reveals　the　correlation　between　the　cognitive　state　of　brain　and　the　location　of　brain　regions.　Compared　with　other　deep　models　and　parameter　optimization　methods,　our　model　achieves　better　detection　accuracy.