Driver fatigue estimation using multimodal deep learning with eeg and eog signals

Driver fatigue is a critical condition that increases accident risk by causing loss of concentration and perceptual limitations. Therefore, researchers have focused on developing systems aimed at the early detection of driver fatigue. In this study, driver fatigue detection was targeted using Differential Entropy–Linear Dynamic System (DE-LDS) based Electroencephalography (EEG) features and Electrooculography (EOG) features provided by the SEED-VIG dataset. The feature pool employed in the study was enriched through feature engineering methods such as band ratios, temporal delta components, hemispheric asymmetry, and moving standard deviation. Moreover, by eliminating the need for raw signal processing, the focus was directed toward the temporal variation patterns of meaningful features. Experimental results demonstrated that predictions based solely on EEG data remained limited, whereas the inclusion of EOG data significantly improved performance.These findings reveal that EEG and EOG signals provide complementary information in the prediction of driver fatigue. The proposed multimodal approach exhibited greater robustness against inter-subject variability compared to unimodal systems and offered a more generalizable monitoring framework.