Discriminating Stress and Mental Workload Using Multimodal Physiological Signals

This is the authors' abstract. We don't add key points for this paper.

Stress and mental workload (MWL) frequently co-occur and share physiological signatures, reducing discriminability and limiting cross-subject generalisation. To address this, we implemented an immersive virtual reality (VR) adaptation of the Trier Social Stress Test (TSST) using a 2×2 factorial design that manipulated psychosocial stress (high/low) and MWL (high/low), enabling controlled evaluation under co-occurrence. We recorded electroencephalography (EEG), electrodermal activity, heart rate and heart rate variability, and pupillometry in 44 participants. Classification was used not as a methodological contribution or classifier benchmark, but as a probe of cross-subject physiological discriminability under co-occurring stress and MWL. Using leave-one-subject-out cross-validation, MWL was classified above chance across stress levels, whereas stress did not generalise beyond chance despite robust subjective effects. EEG spectral ratio features showed greater stability and stronger MWL discriminability than absolute power measures, while autonomic features showed limited state specificity. These findings indicate that physiological discriminability under co-occurring demands is state dependent. Under immersive, socially evaluative conditions, MWL exhibits modest but generalisable neural features across individuals, whereas stress appears substantially more sensitive to contextual and individual variation. This work clarifies the conditions under which cross-subject physiological generalisation is feasible and informs the evaluation and modelling of co-occurring cognitive-affective states in realistic settings.