Accurate assessment of cognitive load is vital in cognitive research and human-machine interaction. This study investigates a multimodal approach for classifying graded cognitive load levels using cardiovascular signals derived from photoplethysmography (PPG) and impedance plethysmography (IPG). Data were collected from 15 healthy adults performing mental arithmetic tasks of increasing difficulty (Rest, Level 1, Level 2, and Level 3). Carotid PPG was used as a global indicator of cerebral perfusion, while frontal IPG captured localized changes in regional blood volume. Machine learning algorithms, including Decision Trees, Random Forest, and XGBoost, were applied to discriminate between workload levels. Among these models, Random Forest achieved the highest performance, reaching 96% accuracy in subject-dependent classification. Subject-independent accuracy was lower (66%), reflecting substantial inter-subject variability. IPG-derived features were among the most influential contributors to workload discrimination, highlighting the role of localized neurovascular responses to cognitive demand. These findings support the potential of PPG-IPG fusion as a noninvasive and physiologically grounded technique for continuous monitoring of cognitive workload.
Huynh et al. (2026) studied this question.