OBJECTIVE: Driver fatigue poses a serious threat to road safety. This study presents a method for detecting driving fatigue and initiating wakefulness based on electroencephalogram (EEG) signals. METHODS: A total of 1,230 EEG samples were collected from 30 drivers during simulated driving. These samples were decomposed into θ, α, and β frequency bands using Discrete Wavelet Transform (DWT). A brain functional connectivity network was constructed based on the Phase-Lag Index (PLI) to extract features. CNN-LSTM, Transformer, and logistic regression models were trained to evaluate arousal effects under visual, olfactory, auditory single-modality, and multimodal conditions. RESULTS: < 0.05), with the multimodal visual,auditory,and olfactory approach yielding the strongest effect, reducing fatigue levels by an average of 2.633 points. The arousal effect was more pronounced at higher fatigue levels. CONCLUSIONS: This study provides a theoretical foundation for fatigue monitoring and intervention in intelligent cockpits and autonomous driving systems.
Lu et al. (Thu,) studied this question.