Stress assessment with EEG and machine learning in affective VR environments

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.