Driver drowsiness remains a primary cause of traffic accidents, necessitating the development of real-time, reliable detection systems to ensure road safety. This study presents a Modified TSception architecture designed for the robust assessment of driver fatigue using Electroencephalography (EEG). The model introduces a novel hierarchical architecture that surpasses the original TSception by implementing a five-layer temporal refinement strategy to capture multi-scale brain dynamics. A key innovation is the use of Adaptive Average Pooling, which provides the structural flexibility to handle varying EEG input dimensions, and a two - stage fusion mechanism that optimizes the integration of spatiotemporal features for improved stability. When evaluated on the SEED-VIG dataset and compared against established methods - including SVM, Transformer, EEGNet, ConvNeXt, LMDA-Net, and the original TSception - the Modified TSception achieves a comparable accuracy of 83.46% (vs. 83.15% for the original). Critically, the proposed model exhibits a substantially reduced confidence interval (0.24 vs. 0.36), signifying a marked improvement in performance stability. Furthermore, the architecture's generalizability is validated on the STEW mental workload dataset, where it achieves state-of-the-art results with 95.93% and 95.35% accuracy for 2-class and 3-class classification, respectively. These improvements in consistency and cross-task generalizability underscore the effectiveness of the proposed modifications for reliable EEG-based monitoring of drowsiness and mental workload.

翻译：驾驶员困倦仍是交通事故的主要原因，亟需开发实时、可靠的检测系统以确保道路安全。本研究提出一种改进型TSception架构，旨在利用脑电图（EEG）对驾驶员疲劳进行稳健评估。该模型引入了一种新颖的分层架构，通过实施五层时间细化策略来捕捉多尺度大脑动态，从而超越了原始TSception。其核心创新在于采用自适应平均池化，提供了处理不同EEG输入维度的结构灵活性，以及一个两阶段融合机制，优化了时空特征的整合以提升稳定性。在SEED-VIG数据集上进行评估，并与现有方法（包括SVM、Transformer、EEGNet、ConvNeXt、LMDA-Net及原始TSception）进行比较后，改进型TSception取得了83.46%的准确率（原始版本为83.15%）。关键的是，所提模型的置信区间显著缩小（0.24对比0.36），标志着性能稳定性得到显著提升。此外，该架构的泛化能力在STEW心理负荷数据集上得到验证，其在二分类和三分类任务中分别达到了95.93%和95.35%的准确率，取得了最先进的结果。这些在一致性和跨任务泛化能力方面的改进，凸显了所提修改方案对于基于EEG的困倦与心理负荷可靠监测的有效性。