Research on the application of the Sleep EEG Net model based on domain adaptation transfer in the detection of driving fatigue

Fatigue detection in driving faces challenges stemming from data scarcity and difficulty in data acquisition, which poses a significant challenge to traditional fatigue detection methods. To address this issue, this study introduces a Sleep EEG Net model based on domain adaptation transfer learning. This model was pre-trained using the publicly available Sleep-EDF dataset, and domain adaptation transfer training techniques were employed to train the feature extractor of the pre-trained model, enabling cross-domain knowledge transfer. As a result, the model has been successfully applied to the task of fatigue detection in driving with only a limited amount of fatigue driving data. Experimental results demonstrate that this approach achieves a recognition accuracy of 91.5% in fatigue detection tasks. Furthermore, the model exhibits strong generalization capabilities and robustness, achieving high recognition accuracy in both simulated and real driving environments, thereby validating its effectiveness in practical applications.