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

Dawoon Jung, Junggu Choi, Jeongjae Kim, Seoyoung Cho, Sanghoon Han

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Article number2158
JournalInternational journal of environmental research and public health
Issue number4
Publication statusPublished - 2022 Feb 1

Bibliographical note

Funding Information:
Funding: This research was supported by the Brain Research Program through the National Re‐ search Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2017M3C7A1029485) and partially supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (NRF‐2019R1A2C1007399).

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

  • Pollution
  • Public Health, Environmental and Occupational Health
  • Health, Toxicology and Mutagenesis


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