Effects of virtual reality visual experience on brain functional network_Journal of Biomedical Engineering

Authors：

ZHANG Tianheng ^1,2 , WANG Lei ^1,2 ,  GUO Miaomiao ^1,2 , XU Guizhi ^1,2

1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, P.R.China;
2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, P.R.China;

Corresponding author：

GUO Miaomiao, Email: gmm@hebut.edu.cn

Keywords：

electroencephalogram; virtual reality; brain network; brain fatigue

DOI：

10.7507/1001-5515.201812027

Video：

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With the wide application of virtual reality technology and the rapid popularization of virtual reality devices, the problem of brain fatigue caused by prolonged use has attracted wide attention. Sixteen healthy subjects were selected in this study. And electroencephalogram (EEG) signals were acquired synchronously while the subjects watch videos in similar types presented by traditional displayer and virtual reality separately. Two questionnaires were conducted by all subjects to evaluate the state of fatigue before and after the experiment. The mutual correlation method was selected to construct the mutual correlation brain network of EEG signals before and after watching videos in two modes. We also calculated the mutual correlation coefficient matrix and the mutual correlation binary matrix and compared the average of degree, clustering coefficient, path length, global efficiency and small world attribute during two experiments. The results showed that the subjects were easier to get fatigue by watching virtual reality video than watching video presented by traditional displayer in a certain period of time. By comparing the characteristic parameters of brain network before and after watching videos, it was found that the average degree value, the average clustering coefficient, the average global efficiency and the small world attribute decreases while the average path length value increased significantly. In addition, compared to traditional plane video, the characteristic parameters of brain network changed more greatly after watching the virtual reality video with a significant difference (P < 0.05). This study can provide theoretical basis and experimental reference for analyzing and evaluating brain fatigue induced by virtual reality visual experience.

Citation： ZHANG Tianheng, WANG Lei, GUO Miaomiao, XU Guizhi. Effects of virtual reality visual experience on brain functional network. Journal of Biomedical Engineering, 2020, 37(2): 251-261. doi: 10.7507/1001-5515.201812027 Copy

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4.	Suzumura A. Visual fatigue. Ganka, 1981, 23: 799-904.
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9.	杨硕, 冀亚坤, 李润泽, 等. 基于theta-gamma相位幅值耦合的脑疲劳信息传递整合机制研究. 生物医学工程学杂志, 2018, 35(5): 672-678.
10.	Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci, 2009, 10(3): 186-198.
11.	He B, Dai Y, Astolfi L, et al. eConnectome: a MATLAB toolbox for mapping and imaging of brain functional connectivity. J Neurosci Methods, 2011, 195(2): 261-269.
12.	Ewen J B, Lakshmanan B M, Hallett M, et al. Dynamics of functional and effective connectivity within human cortical motor control networks. Clinical Neurophysiology, 2015, 126(5): 987-996.
13.	蒋田仔, 刘勇, 李永辉. 脑网络: 从脑结构到脑功能. 生命科学, 2009, 21(2): 181-188.
14.	李亚鹏. 大脑功能网络及其动力学研究. 武汉: 华中科技大学, 2014.
15.	Weiss S A, Bassett D S, Rubinstein D, et al. Functional brain network characterization and adaptivity during task practice in healthy volunteers and people with schizophrenia. Frontiers in Human Neuroscience, 2011, 5: 81.
16.	Simonyan K, Ostuni J, Ludlow C L, et al. Functional but not structural networks of the human laryngeal motor cortex show left hemispheric lateralization during syllable but not breathing production. Journal of Neuroscience, 2009, 29(47): 14912-14923.
17.	Yaffe R B, Borger P, Megev P, et al. Physiology of functional and effective networks in epilepsy. Clinical Neurophysiology, 2015, 126(2): 227-236.
18.	陈润格, 张露, 王晓璐, 等. 自适应多维N-back认知负荷模型下的脑网络研究. 航天医学与医学工程, 2015, 28(6): 391-396.
19.	孟桂芳, 许敏鹏, 张春翠, 等. 一种与N-Back诱发脑力疲劳相关的特异性脑电网络结构. 中国生物医学工程学报, 2017, 36(2): 143-149.
20.	Guo Jie, Weng Dongdong, Duh H B L, et al. Effects of using HMDs on visual fatigue in virtual environments//19th IEEE Virtual Reality Conference (VR), Los Angeles: IEEE, 2017: 456-460.
21.	许昊骏. 面向VR影视的疲劳度和关注度的评估方法. 上海: 上海大学, 2018.
22.	Liu Yuting, Wu Shanglin, Chou Kuangpen, et al. Driving fatigue prediction with pre-event electroencephalography (EEG) via a recurrent fuzzy neural network//2016 IEEE International Conference on Fuzzy Systems, Vancouver: IEEE, 2016. DOI: 10.1109/FUZZ-IEEE.2016.7738006.
23.	Rechichi C, Scullica L. Asthenopia and monitor characteristics. J Fr Ophtalmol, 1990, 13(8-9): 456-460.
24.	尹宁. 基于脑电的磁刺激穴位复杂脑功能网络研究. 天津: 河北工业大学, 2013.
25.	Albert R, Barabasi A L. Statistical mechanics of complex networks. Rev Mod Phys, 2002, 74(1): 47-97.
26.	赵静. 基于互信息的EEG脑功能网络研究. 西安: 西安电子科技大学, 2013.
27.	梁夏, 王金辉, 贺永. 人脑连接组研究: 脑结构网络和脑功能网络. 科学通报, 2010, 55(16): 1565-1583.
28.	Watts D J, Strogatz S H. Collective dynamics of 'small-world' networks. Nature, 1988, 393(6684): 440-442.
29.	Friston K J, Frith C D, Liddle P F, et al. Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab, 1993, 13(1): 5-14.
30.	Zhang C, Cong F, Wang H. Driver fatigue analysis based on binary brain networks//International Conference on Information Science and Technology (ICIST), Da Nang, 2017: 485-489.
31.	Li Jian, Chen Chunxiao, Liu Yuping, et al. Small-world brain functional network altered by watching 2D/3DTV. Journal of Visual Communication and Image Representation, 2016, 38: 433-439.
32.	Seok H O, Taeg K W. Study on relieving VR contents user’s fatigue degree using aroma by measuring EEG//The 9th International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, 2018: 568-570.

1. Bailenson J, Patel K, Nielsen A, et al. The effect of interactivity on learning physical actions in virtual reality. Media Psychol, 2008, 11(3): 354-376.
2. Bailenson J N, Yee N, Blascovich J, et al. The use of immersive virtual reality in the learning sciences: digital transformations of teachers, students, and social context. Journal of the Learning Sciences, 2008, 17(1): 102-141.
3. Bohil C J, Alicea B, Biocca F A. Virtual reality in neuroscience research and therapy. Nature Reviews Neuroscience, 2011, 12: 752-762.
4. Suzumura A. Visual fatigue. Ganka, 1981, 23: 799-904.
5. Zhang C, Zheng C, Yu X. Evaluation of mental fatigue based on multipsychophysiological parameters and kernel learning algorithms. Chin Sci Bull, 2008, 53(12): 1847-1853.
6. 刘建平, 张崇, 郑崇勋, 等. 基于多导脑电复杂性测度的脑疲劳分析. 西安交通大学学报, 2008, 42(12): 1555-1559.
7. 王利, 艾玲梅, 王四万, 等. 驾驶疲劳脑电信号节律的特征分析. 生物医学工程学杂志, 2012, 29(4): 629-633.
8. 张力新, 张春翠, 何峰, 等. 体脑疲劳交互影响及神经机制研究进展. 生物医学工程学杂志, 2015, 32(5): 1135-1140.
9. 杨硕, 冀亚坤, 李润泽, 等. 基于theta-gamma相位幅值耦合的脑疲劳信息传递整合机制研究. 生物医学工程学杂志, 2018, 35(5): 672-678.
10. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci, 2009, 10(3): 186-198.
11. He B, Dai Y, Astolfi L, et al. eConnectome: a MATLAB toolbox for mapping and imaging of brain functional connectivity. J Neurosci Methods, 2011, 195(2): 261-269.
12. Ewen J B, Lakshmanan B M, Hallett M, et al. Dynamics of functional and effective connectivity within human cortical motor control networks. Clinical Neurophysiology, 2015, 126(5): 987-996.
13. 蒋田仔, 刘勇, 李永辉. 脑网络: 从脑结构到脑功能. 生命科学, 2009, 21(2): 181-188.
14. 李亚鹏. 大脑功能网络及其动力学研究. 武汉: 华中科技大学, 2014.
15. Weiss S A, Bassett D S, Rubinstein D, et al. Functional brain network characterization and adaptivity during task practice in healthy volunteers and people with schizophrenia. Frontiers in Human Neuroscience, 2011, 5: 81.
16. Simonyan K, Ostuni J, Ludlow C L, et al. Functional but not structural networks of the human laryngeal motor cortex show left hemispheric lateralization during syllable but not breathing production. Journal of Neuroscience, 2009, 29(47): 14912-14923.
17. Yaffe R B, Borger P, Megev P, et al. Physiology of functional and effective networks in epilepsy. Clinical Neurophysiology, 2015, 126(2): 227-236.
18. 陈润格, 张露, 王晓璐, 等. 自适应多维N-back认知负荷模型下的脑网络研究. 航天医学与医学工程, 2015, 28(6): 391-396.
19. 孟桂芳, 许敏鹏, 张春翠, 等. 一种与N-Back诱发脑力疲劳相关的特异性脑电网络结构. 中国生物医学工程学报, 2017, 36(2): 143-149.
20. Guo Jie, Weng Dongdong, Duh H B L, et al. Effects of using HMDs on visual fatigue in virtual environments//19th IEEE Virtual Reality Conference (VR), Los Angeles: IEEE, 2017: 456-460.
21. 许昊骏. 面向VR影视的疲劳度和关注度的评估方法. 上海: 上海大学, 2018.
22. Liu Yuting, Wu Shanglin, Chou Kuangpen, et al. Driving fatigue prediction with pre-event electroencephalography (EEG) via a recurrent fuzzy neural network//2016 IEEE International Conference on Fuzzy Systems, Vancouver: IEEE, 2016. DOI: 10.1109/FUZZ-IEEE.2016.7738006.
23. Rechichi C, Scullica L. Asthenopia and monitor characteristics. J Fr Ophtalmol, 1990, 13(8-9): 456-460.
24. 尹宁. 基于脑电的磁刺激穴位复杂脑功能网络研究. 天津: 河北工业大学, 2013.
25. Albert R, Barabasi A L. Statistical mechanics of complex networks. Rev Mod Phys, 2002, 74(1): 47-97.
26. 赵静. 基于互信息的EEG脑功能网络研究. 西安: 西安电子科技大学, 2013.
27. 梁夏, 王金辉, 贺永. 人脑连接组研究: 脑结构网络和脑功能网络. 科学通报, 2010, 55(16): 1565-1583.
28. Watts D J, Strogatz S H. Collective dynamics of 'small-world' networks. Nature, 1988, 393(6684): 440-442.
29. Friston K J, Frith C D, Liddle P F, et al. Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab, 1993, 13(1): 5-14.
30. Zhang C, Cong F, Wang H. Driver fatigue analysis based on binary brain networks//International Conference on Information Science and Technology (ICIST), Da Nang, 2017: 485-489.
31. Li Jian, Chen Chunxiao, Liu Yuping, et al. Small-world brain functional network altered by watching 2D/3DTV. Journal of Visual Communication and Image Representation, 2016, 38: 433-439.
32. Seok H O, Taeg K W. Study on relieving VR contents user’s fatigue degree using aroma by measuring EEG//The 9th International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, 2018: 568-570.

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