Enhancing Cross-Dataset Mental Workload Detection Using Electrodermal Activity and Domain Adaptation

Mental workload assessment using physiological signals has gained increasing attention for applications in human–computer interaction and occupational monitoring. Among these signals, electrodermal activity (EDA) is widely recognised as a reliable indicator of sympathetic activation associated with cognitive effort. However, most existing machine learning- based approaches are evaluated within a single dataset, limiting their generalisability across different populations and experimental conditions. This study investigates the cross-dataset performance of machine learning models for mental workload detection using EDA features. Two independent datasets were employed, and a cross-dataset evaluation framework was adopted to simulate realistic deployment scenarios under domain shift. Three classifiers (Random Forest, XGBoost, and Support Vector Classifier (SVC)) were evaluated, together with two domain adaptation techniques: Correlation Alignment (CORAL) and Subspace Alignment (SA). The results show that model performance is strongly dependent on the direction of transfer, with a notable performance drop when generalising across datasets. Domain adaptation improved performance in several configurations, particularly for SVC with CORAL, achieving the best overall F1-score (0.815). However, improvements were not consistent across all models and target domains. Overall, this study highlights the challenges of cross-dataset generalisation in EDA-based workload detection and demonstrates the potential, yet limited robustness, of domain adaptation techniques in mitigating distribution shifts.