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.

翻译：驾驶员困倦是引发交通事故的主要原因，需要实时、可靠的检测系统以确保道路安全。本研究提出一种改进的TSception架构，用于基于脑电图（EEG）对驾驶员疲劳与心理负荷进行鲁棒性评估。该模型引入了五层分层时间细化策略以捕捉多尺度大脑动态，超越了原始TSception的三层方法。关键创新包括：采用自适应平均池化（ADP）以适应不同EEG维度的结构灵活性，以及采用两阶段融合机制以优化时空特征整合，从而提升稳定性。在SEED-VIG数据集上的评估显示，改进的TSception达到83.46%的准确率，与原始模型（83.15%）相当，但置信区间显著缩小（0.24对比0.36），表明其具有更好的性能稳定性。该架构的泛化能力在STEW心理负荷数据集上得到进一步验证，在二分类和三分类任务中分别达到了95.93%和95.35%的最先进准确率。这些结果表明，所提出的改进提高了模型的一致性与跨任务泛化能力，使其成为基于EEG的安全监测的可靠框架。