Eye-Tracking as an Indicator of Depressive Emotional States in Virtual Reality: An Explainable Artificial Intelligence Approach
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DOI:
https://doi.org/10.5281/zenodo.14188690Keywords:
Emotion Recognition, Eye-Tracking, Explainable Artificial Intelligence, Shap, InterpretabilityAbstract
This study explores the intricate relationship between eye-tracking metrics and emotional states,
with a particular focus on depressive emotions, including sadness, depression, and boredom. Leveraging
the VR Eyes: Emotions Dataset (VREED), a publicly available dataset that captures eye-tracking data from
participants immersed in 360-degree virtual environments, we examined key eye-movement features such
as saccades, micro-saccades, fixations, and blinks. By applying the Circumplex Model of Affect (CMA),
we categorize emotional states along dimensions of arousal and valence, facilitating a nuanced analysis of
affective responses. An Extra Trees Classifier was employed as our primary machine learning model to
predict emotional states based on eye-tracking metrics, and we used Explainable Artificial Intelligence
(XAI) techniques to interpret the model's decisions. These XAI techniques, such as SHAP, reveal the
individual contributions of each feature, highlighting the critical role of micro-saccades and fixations as
predictors of depressive states. Our findings suggest that eye-tracking metrics may serve as objective
indicators of emotional experiences. This research underscores the potential of integrating eye-tracking data
and machine learning within virtual environments as a valuable approach for advancing emotional
assessment in mental health contexts.
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