# Assessing Region-Level EEG Contributions to Cognitive Workload Prediction

arXiv: https://arxiv.org/abs/2606.02598 · HTML: https://mlsanity.com/paper/2606.02598/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 23 May 2026.

## Builds on (most cited first, 5 of 5)

- [Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods](https://mlsanity.com/paper/2212.08744/) (2022)
- [Measuring Cognitive Workload Using Multimodal Sensors](https://mlsanity.com/paper/2205.04235/) (2022)
- [MOCAS: A Multimodal Dataset for Objective Cognitive Workload Assessment on Simultaneous Tasks](https://mlsanity.com/paper/2210.03065/) (2022)
- [Optimal EEG Electrode Set for Emotion Recognition From Brain Signals: An Empirical Quest](https://mlsanity.com/paper/2311.17204/) (2023)
- [Modeling cognitive load as a self-supervised brain rate with electroencephalography and deep learning](https://mlsanity.com/paper/2209.10992/) (2022)

## Abstract (arXiv)

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.