Research on the Prediction of Driver Fatigue State Based on EEG Signals

Objective: Predicting driver fatigue states is crucial for traffic safety. This study develops a deep learning model based on electroencephalogaphy (EEG) signals and multi-time-step historical data to predict the next time-step fatigue indicator percentage of eyelid closure (PERCLOS), while exploring the impact of different EEG features on prediction performance. Approach: A CTL-ResFNet model integrating CNN, Transformer Encoder, LSTM, and residual connections is proposed. Its effectiveness is validated through two experimental paradigms: Leave-One-Out Cross-Validation (LOOCV) and pretraining-finetuning, with comparisons against baseline models. Additionally, the performance of four EEG features differential entropy, α/β band power ratio, wavelet entropy, and Hurst exponent—is evaluated, using RMSE and MAE as metrics. Main Results: The combined input of EEG and PERCLOS significantly outperforms using PERCLOS alone validated by LSTM, CTL-ResFNet surpasses baseline models under both experimental paradigms, In LOOCV experiments, the α/β band power ratio performs best, whereas differential entropy excels in pretraining-finetuning. Significance: This study provides a high-performance hybrid deep learning framework for fatigue state prediction and reveals the applicability differences of EEG features across experimental paradigms, offering guidance for feature selection and model deployment in practical applications.