This study aims to create a fatigue recognition system that utilizes electroencephalogram (EEG) signals to assess a driver's physiological and mental state, with the goal of minimizing the risk of road accidents by detecting driver fatigue regardless of physical cues or vehicle attributes. A fatigue state recognition system was developed using transfer learning applied to partial ensemble averaged EEG power spectral density (PSD). The study utilized layer-wise relevance propagation (LRP) analysis to identify critical cortical regions and frequency bands for effective fatigue discrimination. A total of 21 participants were included in the study, and data augmentation techniques were used to enhance the system's classification accuracy. The results indicate a significant improvement in classification accuracy, particularly with the application of data augmentation. The classification accuracies were 99.2 ± 2.3% for the training data, 97.9 ± 3.1% for the validation data, and 96.9 ± 3.3% for the test data. This study advances the development of personalized EEG-based fatigue monitoring systems that have the potential to improve road safety and reduce accidents. The findings highlight the utility of EEG signals in detecting fatigue and the benefits of data augmentation in improving system performance. Further research is recommended to optimize data augmentation strategies and enhance the scalability and efficiency of the system.
Supplementary information: The online version contains supplementary material available at 10.1007/s13534-024-00431-x.
Keywords: Convolutional neural network; Data augmentation; Electroencephalogram; Fatigue; Power spectral density; Transfer learning.
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