Title Development of a Prediction Model for EEG-based Relaxation-arousal State of Users Experiencing a Virtual Reality Space
Authors 김상희(Kim, Sang-Hee) ; 추승연(Choo, Seung-Yeon)
DOI https://doi.org/10.5659/JAIK.2022.38.11.107
Page pp.107-118
ISSN 2733-6247
Keywords Virtual Reality Space; EEG; Relaxation-Arousal; Prediction Model; Machine learning
Abstract This study was carried out to develop a model that can predict a user's relaxation-arousal state by using electroencephalogram (EEG) data and machine learning algorithms of users experiencing a virtual reality space. Specific ways were proposed to improve the prediction accuracy of this model. Upon learning about this model, the prediction performance was compared while changing the hyperparameter conditions of each model using supervised learning-based machine learning models suitable for the development of predictive models known as the random forest, support vector machine, and artificial neural network algorithms. As a result, the random forest model had the highest prediction accuracy when there were 300 trees, the support vector machine model when a sigmoid kernel was applied, and the artificial neural network model when there were five hidden layers; these results confirmed that each optimal parameter condition could be met. Each model was learned by applying the feature extraction method suggested in feature engineering to derive an improvement method in the prediction performance of each model. The results revealed that when the frequency-specific statistics and filtering-based feature extraction method was applied, the prediction performance improved in the random forest and artificial neural network models. Additionally, it was shown that the machine learning models that could best predict the relaxation-arousal state from the EEG data of users experiencing a virtual reality space was the artificial neural network model with five hidden layers applied with the frequency-specific statistics and filtering-based feature processing method; its predictive accuracy was 70.21%. The results of this study could be useful basic data to implement an automated system that evaluates the design drafts of a healing space by utilizing virtual reality and EEG data.