Cross-subject Driver Fatigue Detection Based on Local and Global Feature Integrated Network

Driver fatigue detection plays a crucial role in reducing traffic accidents.Electroencephalogram(EEG) signals,recognized as effective indicators that directly reflect a driver's mental state,are widely acknowledged as valuable tools for fatigue detection.However,the inherent high noise characteristics of EEG signals and their significant variability across individuals pose considerable challenges for cross-subject driver fatigue detection.To address these challenges,this paper proposes an integrated network based on local feature processing and global feature processing to extract features from EEG signals,aiming at overcoming the issues in cross-subject fatigue detection.When applied to the SEED-VIG dataset for a cross-subject three-class detection task,this model achieves an accuracy of 61.34%,significantly surpassing baseline methods.To enhance the performance of the model further,it employs and refines transfer learning methods,resulting in a 13.35% increase in model accuracy for the cross-subject three-class detection task.Overall,this study has demonstrated promising results in EEG-based cross-subject driver fatigue detection,offering new strategies for future studies in this direction.