Real-time affect detection in virtual reality: a technique based on a three-dimensional model of affect and EEG signals - Pinilla, Andres; Voigt-Antons, Jan-Niklas; Garcia, Jaime; Raffe, William; Möller, Sebastian (Frontiers in Virtual Reality, 2023)
This manuscript explores the development of a technique for near real-time affect detection of Virtual Reality (VR) users. The technique was tested in an experiment with 18 participants who observed 16 videos with emotional content inside a VR home theater, while their electroencephalography (EEG) signals were recorded. Participants evaluated their affective response towards the videos in terms of three dimensional model of affect. Two variants of the technique were analysed. The difference between both variants was the method used for feature selection. In the first variant, features extracted from the EEG signals were selected using Linear Mixed-Effects (LME) models. In the second variant, features were selected using Recursive Feature Elimination with Cross Validation (RFECV). Random forest was used in both variants to build the classification models. The results indicate that the feature selection method does not influence the accuracy of the classification models. Therefore, both variations (LME and RFECV) seem equally reliable for detecting affective states of VR users. The mean accuracy of the classification models was between 87% and 93%.