Cross-Driver Domain Generalization for Improved Drowsiness Recognition Based on EEG Signals

• Published in: IEEE transactions on intelligent transportation systems pp. 1 - 18
• Main Authors: Li, Guofa, Zhang, Long, Ouyang, Delin, Li, Qingkun, Li, Zhenning, Li, Shengbo Eben, Green, Paul, Olaverri-Monreal, Cristina
• Format: Journal Article
• Language: English
• Published: IEEE 2025
• Subjects: Accuracy; Brain modeling; brain regions; Convolutional neural networks; domain generalization; Drowsiness recognition; Electroencephalography; electroencephalography (EEG); Electronic mail; Fatigue; Feature extraction; frequency bands; Frequency-domain analysis; Training; Vehicles
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2025.3581395

Designing brain-computer interface systems for electroencephalogram (EEG)-based driver drowsiness recognition remains a significant challenge due to the significant variation in EEG signals across subjects and recording sessions. To address this problem, this paper develops a novel two-stage information transfer strategy framework for domain generalization. The framework has two domain mappers to reduce the distribution differences of EEG features from different individuals, a mapper mix block for generating hybrid mapping features, and a domain adversarial neural network (DANN) for drowsiness recognition based on hybrid EEG features. In the process of DANN to capture common features, we additionally employ two models based on self-attention mechanism to capture domain-invariant attention relationships between electrode channels and between frequency bands. Experimental results show that the proposed framework achieves an average accuracy of 81.34% in the leave-one-out cross validation for driver drowsiness recognition, which is higher than the state-of-the-art model with the number of 79.37%. In addition, we explore the impact of EEG features from different frequency bands and brain regions on this cross-subject task. The results show that EEG features from delta, theta and alpha bands can achieve much better performance than the other two bands, and features from the frontal lobe region perform better than the other regions. These findings reveal domain-invariant features and their relationships with brain regions and frequency bands, enhancing our understanding of the underlying messages of EEG signals.