Assessing Region-Level EEG Contributions to Cognitive Workload Prediction
For researchers designing EEG-based workload monitoring systems, this work provides empirical evidence that frontal electrodes alone can achieve superior or comparable performance, enabling more efficient and generalizable systems.
This paper introduces a region-level evaluation framework to assess EEG contributions to cognitive workload prediction across four datasets. Frontal electrode groups outperform full-scalp baselines by 15-20% in relative rank position, while using fewer electrodes, indicating that workload-relevant information is most consistently retained in frontal and fronto-central regions.
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.