Relaxation Degree Analysis Using Frontal Electroencephalogram Under Virtual Reality Relaxation Scenes
Increasing social pressure enhances the psychological burden on individuals, and the severity of depression can no longer be ignored. The characteristics of high immersion and interactivity enhance virtual reality (VR) application in psychological therapy. Many studies have verified the effectivenes...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-09-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2021.719869/full |
| _version_ | 1818717995415896064 |
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| author | Yue Zhang Lulu Zhang Haoqiang Hua Jianxiu Jin Lingqing Zhu Lin Shu Xiangmin Xu Xiangmin Xu Feng Kuang Yunhe Liu |
| author_facet | Yue Zhang Lulu Zhang Haoqiang Hua Jianxiu Jin Lingqing Zhu Lin Shu Xiangmin Xu Xiangmin Xu Feng Kuang Yunhe Liu |
| author_sort | Yue Zhang |
| collection | DOAJ |
| description | Increasing social pressure enhances the psychological burden on individuals, and the severity of depression can no longer be ignored. The characteristics of high immersion and interactivity enhance virtual reality (VR) application in psychological therapy. Many studies have verified the effectiveness of VR relaxation therapy, although a few have performed a quantitative study on relaxation state (R-state). To confirm the effectiveness of VR relaxation and quantitatively assess relaxation, this study confirmed the effectiveness of the VR sightseeing relaxation scenes using subjective emotion scale and objective electroencephalogram (EEG) data from college students. Moreover, some EEG features with significant consistent differences after they watched the VR scenes were detected including the energy ratio of the alpha wave, gamma wave, and differential asymmetry. An R-state regression model was then built using the model stacking method for optimization, of which random forest regression, AdaBoost, gradient boosting (GB), and light GB were adopted as the first level, while linear regression and support vector machine were applied at the second level. The leave-one-subject-out method for cross-validation was used to evaluate the results, where the mean accuracy of the framework achieved 81.46%. The significantly changed features and the R-state model with over 80% accuracy have laid a foundation for further research on relaxation interaction systems. Moreover, the VR relaxation therapy was applied to the clinical treatment of patients with depression and achieved preliminary good results, which might provide a possible method for non-drug treatment of patients with depression. |
| first_indexed | 2024-12-17T19:44:00Z |
| format | Article |
| id | doaj.art-b141703d4a764a8787f05e5a46475fcf |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-17T19:44:00Z |
| publishDate | 2021-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-b141703d4a764a8787f05e5a46475fcf2022-12-21T21:34:55ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2021-09-011510.3389/fnins.2021.719869719869Relaxation Degree Analysis Using Frontal Electroencephalogram Under Virtual Reality Relaxation ScenesYue Zhang0Lulu Zhang1Haoqiang Hua2Jianxiu Jin3Lingqing Zhu4Lin Shu5Xiangmin Xu6Xiangmin Xu7Feng Kuang8Yunhe Liu9School of Electronic and Information Engineering, South China University of Technology, Guangzhou, ChinaDepartment of Psychiatry, Guangzhou First People’s Hospital, The Second Affiliated Hospital, South China University of Technology, Guangzhou, ChinaSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou, ChinaSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou, ChinaSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou, ChinaSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou, ChinaSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou, ChinaZhongshan Institute of Modern Industrial Technology of South China University of Technology, Zhongshan, ChinaSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou, ChinaSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou, ChinaIncreasing social pressure enhances the psychological burden on individuals, and the severity of depression can no longer be ignored. The characteristics of high immersion and interactivity enhance virtual reality (VR) application in psychological therapy. Many studies have verified the effectiveness of VR relaxation therapy, although a few have performed a quantitative study on relaxation state (R-state). To confirm the effectiveness of VR relaxation and quantitatively assess relaxation, this study confirmed the effectiveness of the VR sightseeing relaxation scenes using subjective emotion scale and objective electroencephalogram (EEG) data from college students. Moreover, some EEG features with significant consistent differences after they watched the VR scenes were detected including the energy ratio of the alpha wave, gamma wave, and differential asymmetry. An R-state regression model was then built using the model stacking method for optimization, of which random forest regression, AdaBoost, gradient boosting (GB), and light GB were adopted as the first level, while linear regression and support vector machine were applied at the second level. The leave-one-subject-out method for cross-validation was used to evaluate the results, where the mean accuracy of the framework achieved 81.46%. The significantly changed features and the R-state model with over 80% accuracy have laid a foundation for further research on relaxation interaction systems. Moreover, the VR relaxation therapy was applied to the clinical treatment of patients with depression and achieved preliminary good results, which might provide a possible method for non-drug treatment of patients with depression.https://www.frontiersin.org/articles/10.3389/fnins.2021.719869/fullVREEGrelaxation stateregression modelmachine learningdepression therapy |
| spellingShingle | Yue Zhang Lulu Zhang Haoqiang Hua Jianxiu Jin Lingqing Zhu Lin Shu Xiangmin Xu Xiangmin Xu Feng Kuang Yunhe Liu Relaxation Degree Analysis Using Frontal Electroencephalogram Under Virtual Reality Relaxation Scenes Frontiers in Neuroscience VR EEG relaxation state regression model machine learning depression therapy |
| title | Relaxation Degree Analysis Using Frontal Electroencephalogram Under Virtual Reality Relaxation Scenes |
| title_full | Relaxation Degree Analysis Using Frontal Electroencephalogram Under Virtual Reality Relaxation Scenes |
| title_fullStr | Relaxation Degree Analysis Using Frontal Electroencephalogram Under Virtual Reality Relaxation Scenes |
| title_full_unstemmed | Relaxation Degree Analysis Using Frontal Electroencephalogram Under Virtual Reality Relaxation Scenes |
| title_short | Relaxation Degree Analysis Using Frontal Electroencephalogram Under Virtual Reality Relaxation Scenes |
| title_sort | relaxation degree analysis using frontal electroencephalogram under virtual reality relaxation scenes |
| topic | VR EEG relaxation state regression model machine learning depression therapy |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2021.719869/full |
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