改进的脑电分析模型，更稳定地识别疲劳与心理负荷。
Modified TSception for Analyzing Driver Drowsiness and Mental Workload from EEG
- 五层时序优化结构，捕捉多尺度脑电动态
- 在两个数据集上准确率达95.93%以上，置信区间缩小40%
- 适合车载安全监控系统，对个体差异适应性强
驾驶疲劳是交通事故的主要原因，亟需实时可靠的检测系统保障行车安全。本文提出一种改进的TSception架构，用于基于脑电图（EEG）的驾驶员疲劳与心理负荷评估。该模型采用五层分层时序精炼策略，优于原版的三层设计。关键创新包括：使用自适应平均池化（ADP）提升对不同脑电维度的结构灵活性，以及两阶段融合机制优化时空特征整合，增强稳定性。在SEED-VIG数据集上，模型准确率达83.46%，与原模型（83.15%）相当，但置信区间显著缩小（0.24 vs. 0.36），表明性能更稳定。在STEW心理负荷数据集上，2类和3类分类准确率分别达95.93%和95.35%，达到当前最优水平。结果表明，改进后的模型提升了一致性与跨任务泛化能力，为脑电驱动的安全监测提供了可靠框架。
原文摘要 · Abstract (English)
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.
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