Multiple entropy fusion predicts driver fatigue using forehead EEG.

Introduction: One promising research area in traffic safety involves the utilization of an Electroencephalogram (EEG)-based approach to assess driver fatigue in new automatic technology. However, the utilization of forehead channels for identifying fatigue has been underexplored by researchers, which limits practical application. Objectives: To assess driver fatigue using EEG signals from the forehead, we propose a novel method that combines multiple entropies with a stacking model. Methods: We collected EEG signals from 32 subjects and utilized nine entropy measures including approximate entropy, fuzzy entropy, Kolmogorov entropy, permutation entropy, sample entropy, spectral entropy, symbolic transfer entropy, wavelet log energy entropy, and wavelet packet energy entropy for feature extraction. Three fast classifiers were used to build a stacking model, including logistic regression, extreme learning machine, and light gradient boosting machine. The leave-one-out cross-validation method was used to evaluate the performance of the proposed method. Results: Our proposed method yields stronger robustness and better recognition for detecting driver fatigue, demonstrating its potential to enhance current approaches for detecting driver fatigue. Conclusion: The proposed method can provide a more effective way to detect driver fatigue.