Modified TSception for Analyzing Driver Drowsiness and Mental Workload from EEG
This work addresses road safety by providing a more reliable EEG-based monitoring system for driver fatigue and mental workload, though it is incremental as it builds on an existing model.
This study tackled the problem of detecting driver drowsiness and mental workload from EEG signals by proposing a Modified TSception architecture, achieving 83.46% accuracy on a drowsiness dataset and state-of-the-art accuracies up to 95.93% on a mental workload dataset with improved stability.
Driver drowsiness is a leading cause of traffic accidents, necessitating real-time, reliable detection systems to ensure road safety. This study proposes a Modified TSception architecture for robust assessment of driver fatigue and mental workload using Electroencephalography (EEG). The model introduces a five-layer hierarchical temporal refinement strategy to capture multi-scale brain dynamics, surpassing the original TSception's three-layer approach. Key innovations include the use of Adaptive Average Pooling (ADP) for structural flexibility across varying EEG dimensions and a two-stage fusion mechanism to optimize spatiotemporal feature integration for improved stability. Evaluated on the SEED-VIG dataset, the Modified TSception achieves 83.46% accuracy, comparable to the original model (83.15%), but with a significantly reduced confidence interval (0.24 vs. 0.36), indicating better performance stability. The architecture's generalizability was further validated on the STEW mental workload dataset, achieving state-of-the-art accuracies of 95.93% and 95.35% for 2-class and 3-class classification, respectively. These results show that the proposed modifications improve consistency and cross-task generalizability, making the model a reliable framework for EEG-based safety monitoring.