A Review of EEG ‐Based Driver Fatigue Detection: Nonlinear Dynamics, Brain Networks, and Deep Learning Advances

ABSTRACT Driver fatigue has been identified as one of the primary causes of traffic accidents. As long‐duration and high‐load driving becomes increasingly common, the risks of delayed reactions and impaired distance judgment continue to rise. Traditional behavior‐based methods for detecting driver fatigue often exhibit limited stability in complex driving environments. In contrast, electroencephalography (EEG) offers a more reliable detecting method by directly capturing central nervous system activity. This work focuses on fatigue driving detection based on deep learning and EEG, which outlines commonly used public datasets, key preprocessing techniques, feature extraction techniques, performance evaluation metrics, and mainstream deep learning architectures. Based on research progress over the past three years, the use of datasets, published journals, research challenges, and limitations of current methods were analyzed. Future research should improve the model's generalization ability and robustness, introduce richer brain network features, and construct a larger‐scale, high‐quality dataset that closely resembles the real driving environment.