Towards Objective Cognitive Load Quantification with Multi-modal and Soft Facial Electrophysiology

Accurately measuring cognitive load is challenging due to its dynamic nature and often relies on traditional methods, such as self-reports. Electroencephalography (EEG) is often suggested as an objective alternative but this method is sensitive to mechanical artifacts and is often restricted to artificial environments. To address this challenge, we used a unique wearable multi-modal approach integrating soft electrodes for facial electromyography (EMG) and EEG towards objective cognitive load assessment with potential for application in natural conditions. A maze navigation task combined with an N-back memory test was used to induce cognitive load while real-time physiological signals were recorded via a wearable facial electrode array system. Results show that subject-specific EMG channels strongly correlate with subjective cognitive load scores, making it a strong real-time workload marker, while EEG beta entropy decreases over time, reflecting possible cognitive adaptation. Notably, inter-subject variability suggests that personalized modeling is crucial for robust cognitive tracking. These findings demonstrate that facial EMG provides a possible real-time measure of cognitive load, enabling non-invasive monitoring for applications in human-computer interaction, neuroergonomics, and adaptive learning systems, including during movement.Clinical relevanceThis study shows that wearable facial EMG may enable real-time cognitive load assessment, aiding clinicians in monitoring cognitive fatigue, neurocognitive disorders, and workload management in traumatic brain injury, and neurodegenerative diseases.