HatSeqNet: A Sequence-Based Deep Learning Model for Cross-Subject Driver Drowsiness Detection Using Single-Channel EEG Signal

Driver drowsiness represents a significant risk to road safety, being a major contributor to traffic accidents. This study introduces hybrid adaptive temporal sequential networkHatSeqNet, an effectively designed architecture for cross-subject driver drowsiness detection using single-channel electroencephalogram (EEG) signals across different subjects. Leveraging the Mexican hat wavelet transform, EEG signals are transformed into time-scale representations (TSRs), capturing intricate neural patterns and transient dynamics. These TSRs are processed by a hybrid convolutional neural network and recurrent neural network architecture, which effectively models spatial and temporal dependencies. The model is evaluated using leave-one-subject-out cross-validation on a publicly available EEG dataset from a sustained-attention driving task. HatSeqNet achieves a state-of-the-art average accuracy of 88.26%, with precision, recall, and F1-score of 88.87%, 87.63%, and 88.10%, respectively. These metrics surpass those of prior studies on the same SAT dataset. The employment of single-channel EEG enhances the system’s portability, renderingHatSeqNetsuitable for practical vehicular applications. The superior performance of the proposed architecture underscores its potential to enhance road safety through reliable and non-invasive driver drowsiness detection, even without personalized calibration.