AI RESEARCH
Assessing Region-Level EEG Contributions to Cognitive Workload Prediction
arXiv CS.LG
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ArXi:2606.02598v1 Announce Type: new 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.