Driver fatigue detection based on prefrontal EEG using multi-entropy measures and hybrid model

Abstract The development of a real-time monitoring and warning platform for driver fatigue detection is of great importance to avoid traffic accidents and fatalities. Due to the explosive growth of biosignal data, especially electroencephalogram (EEG) signals, generated in the wearable-IoT age, the practical application and recognition stability of these signals should be considered and further studied. The application of EEG signals, especially prefrontal EEG, is a potential key method for effectively determining when a driver is experiencing fatigue, which would promote the development of wearable devices for fatigue warning. In this work, we reported a rapid and effective forehead EEG-based multientropy feature-driven fatigue identification framework using a robust hybrid model. This method investigated and utilized several common entropies to enhance the characteristic quality of EEG signal, and yielded a better expected outcome than those of several classical algorithms due to the hybrid model. The experimental results using prefrontal EEG for assessing driver fatigue showed that the proposed method is convenient and effective, as validated by 32 healthy participants through double-layer nested cross validation. In addition, multi-entropy measures were evaluated for single-channel EEG fatigue detection, and the results showed that wavelet log-energy entropy (WLE) outperformed the other entropy indices with a better recognition rate and higher computational efficiency. We demonstrated that multientropy fusion was able to significantly increase detection quality. This study provided a new strategy of using prefrontal EEG signals to construct a high-efficiency method for detecting driver fatigue, which could possess great potential for real-time single-channel signal analysis.