Key result
Machine learning classification of EEG indicators during a stressful Virtual Reality experiment achieved over 98% accuracy with eXtreme Gradient Boosting and Random Forest classifiers.
Why the study?
The study was conducted to elicit light stress in a Virtual Reality environment and analyze emotional responses using EEG and machine learning.
Can EEG combined with machine learning accurately assess emotional responses and stress in a virtual reality environment?
Can EEG combined with machine learning accurately assess emotional responses and stress in a virtual reality environment?
EEG combined with machine learning classifiers like XGBoost and Random Forest can highly accurately assess stress and emotional responses in virtual reality environments.
This is the authors' abstract. We don't add key points for this paper.
Stress is a reaction that occurs when a person perceives, with or without awareness, an imbalance between requests and available resources. Relying on this definition, we have carried out an experiment in a Virtual Reality environment to elicit (light) stress in the user and analyze the emotional responses with electroencephalography (EEG). The virtual environment is divided in eight parts; in each of them a stressor has been put in action, meaning that in every part the participants perform a task, but a specific resource is missing (time, knowledge, control, salvation, no or too many alternatives, engagement, self-confidence). EEG is used to assess the emotional response with the aid of Valence/Arousal/Dominance/Stress indicators presented in previous literature. Nine indicators calculated for 87 participants, labeled according to self-assessment replies (post-experimental questionnaires), were classified with eXtreme Gradient Boosting , k-Nearest Neighbor, Support Vector Machine and Random Forest classifiers . The lowest results in terms of accuracy were obtained with k-Nearest Neighbor (around 70 %), whilst the highest ones were obtained with eXtreme Gradient Boosting and Random Forest (above 98 %), showing that EEG could be a valuable tool to assess the emotional response in stressful situations, with a particular focus on the Stress indicators.
High accuracy supports EEG-ML feasibility for VR stress assessment; leaves open clinical validation before adoption.
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Marcolin et al. (2025) studied Stress (n=87). Machine learning classification of EEG indicators was evaluated on Classification accuracy of emotional responses. Machine learning classification of EEG indicators during a stressful Virtual Reality experiment achieved over 98% accuracy with eXtreme Gradient Boosting and Random Forest classifiers.
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