Inferring Cognitive State of Pilot's Brain Under Different Maneuvers During Flight

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.

Automated summary

This work proposes an adversarial Bayesian deep network for cognitive detection of pilot fatigue. Batch normalization and data enhancement are used to improve the generalization of neural networks, and a generator is employed to enhance the accuracy of fatigue state recognition. Adversarial noise is added near each brain electrode to reveal the correlation between cognitive state and brain region location.