Classifying mental stress from eye tracking data: deep learning approaches for out-of-the-lab conditions

Eye-tracking signals such as pupil diameter and gaze behavior have been widely used for stress detection, yet most approaches rely on task-specific features, controlled laboratory settings, or multimodal sensor combinations, limiting scalability in less controlled environments. This work investigates whether unimodal eye-tracking time-series data can support task-agnostic stress detection beyond static laboratory tasks. We analyze stress classification across two complementary datasets: a virtual reality goalkeeper task with moderate visuomotor activity and stable recording conditions, and a virtual job interview dataset reflecting less controlled settings with uncalibrated signals. The results show that these signals alone contain informative patterns related to stress-associated autonomic and oculomotor responses. Under favorable conditions, performance reaches up to [Formula: see text] macro-averaged F1-score. At the same time, performance varies substantially across datasets, indicating that effective learning depends strongly on data quality, calibration, signal characteristics, and task design. Overall, the findings demonstrate the potential of unimodal eye tracking as a lower-burden alternative to more complex multimodal systems, while highlighting that reliable stress detection is fundamentally conditioned by the interplay of data, signal representation, and modeling approach.