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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,...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-8872045 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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