EEG-Based Identification of Emotional Neural State Evoked by Virtual Environment Interaction

Dawoon Jung, Junggu Choi, Jeongjae Kim, Seoyoung Cho and Sanghoon Han
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Dawoon Jung: Yonsei Graduate Program in Cognitive Science, Yonsei University, Seoul 03722, Korea
Junggu Choi: Yonsei Graduate Program in Cognitive Science, Yonsei University, Seoul 03722, Korea
Jeongjae Kim: Yonsei Graduate Program in Cognitive Science, Yonsei University, Seoul 03722, Korea
Seoyoung Cho: Yonsei Graduate Program in Cognitive Science, Yonsei University, Seoul 03722, Korea
Sanghoon Han: Yonsei Graduate Program in Cognitive Science, Yonsei University, Seoul 03722, Korea

IJERPH, 2022, vol. 19, issue 4, 1-15

Abstract: Classifying emotional states is critical for brain–computer interfaces and psychology-related domains. In previous studies, researchers have tried to identify emotions using neural data such as electroencephalography (EEG) signals or brain functional magnetic resonance imaging (fMRI). In this study, we propose a machine learning framework for emotion state classification using EEG signals in virtual reality (VR) environments. To arouse emotional neural states in brain signals, we provided three VR stimuli scenarios to 15 participants. Fifty-four features were extracted from the collected EEG signals under each scenario. To find the optimal classification in our research design, three machine learning algorithms (XGBoost classifier, support vector classifier, and logistic regression) were applied. Additionally, various class conditions were used in machine learning classifiers to validate the performance of our framework. To evaluate the classification performance, we utilized five evaluation metrics (precision, recall, f1-score, accuracy, and AUROC). Among the three classifiers, the XGBoost classifiers showed the best performance under all experimental conditions. Furthermore, the usability of features, including differential asymmetry and frequency band pass categories, were checked from the feature importance of XGBoost classifiers. We expect that our framework can be applied widely not only to psychological research but also to mental health-related issues.

Keywords: electroencephalography; virtual reality; emotion recognition; machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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