Modified TSception for Analyzing Driver Drowsiness and Mental Workload from EEG

TL;DR

This paper introduces a Modified TSception model with hierarchical refinement and adaptive pooling for improved EEG-based detection of driver drowsiness and mental workload, demonstrating enhanced stability and accuracy across datasets.

Contribution

The study presents a novel five-layer hierarchical architecture with adaptive pooling and a two-stage fusion mechanism, advancing EEG-based mental state classification.

Findings

Achieved 83.46% accuracy on SEED-VIG with better stability

Attained state-of-the-art 95.93% accuracy on STEW dataset

Enhanced model stability and cross-task generalizability

Abstract

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.