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

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,...

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Autores principales: Jung, Dawoon, Choi, Junggu, Kim, Jeongjae, Cho, Seoyoung, Han, Sanghoon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8872045/
https://www.ncbi.nlm.nih.gov/pubmed/35206341
http://dx.doi.org/10.3390/ijerph19042158
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author Jung, Dawoon
Choi, Junggu
Kim, Jeongjae
Cho, Seoyoung
Han, Sanghoon
author_facet Jung, Dawoon
Choi, Junggu
Kim, Jeongjae
Cho, Seoyoung
Han, Sanghoon
author_sort Jung, Dawoon
collection PubMed
description 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.
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spelling pubmed-88720452022-02-25 EEG-Based Identification of Emotional Neural State Evoked by Virtual Environment Interaction Jung, Dawoon Choi, Junggu Kim, Jeongjae Cho, Seoyoung Han, Sanghoon Int J Environ Res Public Health Article 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. MDPI 2022-02-14 /pmc/articles/PMC8872045/ /pubmed/35206341 http://dx.doi.org/10.3390/ijerph19042158 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jung, Dawoon
Choi, Junggu
Kim, Jeongjae
Cho, Seoyoung
Han, Sanghoon
EEG-Based Identification of Emotional Neural State Evoked by Virtual Environment Interaction
title EEG-Based Identification of Emotional Neural State Evoked by Virtual Environment Interaction
title_full EEG-Based Identification of Emotional Neural State Evoked by Virtual Environment Interaction
title_fullStr EEG-Based Identification of Emotional Neural State Evoked by Virtual Environment Interaction
title_full_unstemmed EEG-Based Identification of Emotional Neural State Evoked by Virtual Environment Interaction
title_short EEG-Based Identification of Emotional Neural State Evoked by Virtual Environment Interaction
title_sort eeg-based identification of emotional neural state evoked by virtual environment interaction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8872045/
https://www.ncbi.nlm.nih.gov/pubmed/35206341
http://dx.doi.org/10.3390/ijerph19042158
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