Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 23, Issue 11, Pages 21729-21739Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2022.3189981
Keywords
Fatigue; Brain modeling; Electroencephalography; Feature extraction; Generative adversarial networks; Electrodes; Bayes methods; Cognitive detection; adversarial Bayesian deep network; adversarial noise; brain power map
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Funding
- National Natural Science Foundation of China [62171274, U1933125, 61825305]
- Shanghai Science and Technology Major Project [2021SHZDZX]
- National Natural Science Foundation of China through the Main Research Project on Machine Behavior and Human-Machine Collaborated Decision Making Methodology [72192820]
- Third Research Project on Human Behavior in Human-Machine Collaboration [72192822]
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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.
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.
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