Automatic Adaptation of Exposure Intensity in VR Acrophobia Therapy, based on Deep Neural Networks.

This paper proposes a real-time Virtual Reality game for treating acrophobia that automatically tailors in-game exposure to heights to the players’ individual characteristics – affective state and physiological features. The elements of novelty are the automatic estimation of fear and the prediction of the next game level based on the electroencephalogram (EEG) and biophysical data – Galvanic Skin Response (GSR) and Heart Rate (HR). Two neural networks have been trained with the data recorded in an experiment where 4 subjects have been in-vivo and virtually exposed to various heights. In order to test the validity of the approach, the same users played the acrophobia game, using two modalities of ex-pressing fear level. After completing a game level, the EEG and biophysical data were averaged and one neural network estimated the current fear score, while the other predicted the next game level. A measure of similarity between the self-estimated fear level during a game epoch and the fear level predicted by the first neural network showed an accuracy rate of 73% and 42% respectively for the two modalities of expressing fear level. 3 out of 4 users succeeded to obtain a fear level of 0 (complete relaxation) in the final game epoch.