Accurate and generalizable estimation of cognitive workload from electroencephalography (EEG) is critical for human-centered and safety-critical systems. Although EEG is widely used for workload assessment, the consistency of region-level EEG contributions across tasks, datasets, and subjects remains unclear. This paper presents a region-level evaluation framework for EEG-based workload prediction in which models are trained and evaluated using features extracted exclusively from electrodes belonging to anatomically defined scalp regions. We perform a large-scale analysis across four publicly available EEG workload datasets spanning diverse task demands, recording hardware, and electrode montages. Region importance is quantified using a model-agnostic, performance-based approach under both mixed-subject and subject-independent evaluation protocols, with results aggregated using a rank-based strategy to ensure robustness across experimental configurations. Across all datasets and subject-independent evaluations, frontal electrode groups outperform the full-scalp baseline by approximately 15-20% in relative rank position while using substantially fewer electrodes. Fronto-central regions exhibit the most stable predictive utility, whereas posterior and occipital regions contribute less consistently across experimental conditions. These findings indicate that workload-relevant EEG information is most consistently retained within frontal and fronto-central electrode groups, supporting the design of efficient and generalizable EEG-based workload monitoring systems.

翻译：从脑电图（EEG）准确且可泛化地估计认知工作负荷，对于以人为中心和安全性至关重要的系统至关重要。尽管脑电图广泛用于工作负荷评估，但区域级脑电贡献在不同任务、数据集和受试者之间的一致性仍不清楚。本文提出了一种基于脑电图的工作负荷预测的区域级评估框架，其中模型仅使用从属于解剖学定义的颅顶区域的电极提取的特征进行训练和评估。我们对四个公开可用的脑电图工作负荷数据集进行了大规模分析，这些数据集涵盖了不同的任务需求、记录硬件和电极布局。区域重要性使用一种模型无关、基于性能的方法进行量化，并在混合受试者和受试者独立两种评估协议下进行，结果采用基于排名的策略进行聚合，以确保在实验配置中的稳健性。在所有数据集和受试者独立评估中，额叶电极组在相对排名位置上优于全颅基线约15-20%，同时使用的电极数量显著减少。额中央区域表现出最稳定的预测效用，而枕叶和顶叶区域在实验条件下的贡献一致性较低。这些发现表明，与工作负荷相关的脑电信息最持续地保留在额叶和额中央电极组内，支持设计高效且可泛化的基于脑电图的工作负荷监测系统。