基于源空间功能连通性分析的虚拟现实晕动症检测研究

doi:10.3969/j.issn.0258-8021.2025.03.002

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Abstract Virtual reality motion sickness (VRMS) is a key factor hindering the development of the virtual reality (VR) industry. To mitigate the adverse user experiences and health risks associated with VRMS, it is essential to detect its onset. This study explored the neural mechanisms underlying VRMS by analyzing the functional connectivity in the brain’s cortical regions, aiming to provide effective biomarkers for VRMS detection. The research employed frequency-domain source localization, phase lag index (PLI) calculation, and graph theory-based quantification of brain functional networks to extract electroencephalogram (EEG) features related to VRMS. The PLI results indicated significant differences in the connectivity strength within the theta and alpha frequency bands between VRMS and normal states (P<0.05). Additionally, graph theory metrics showed a significant increase in the node efficiency and transitivity in the theta band (P<0.01) and a significant increase in the clustering coefficient and node efficiency in the alpha band (P<0.01) during VRMS episodes. Finally, support vector machine (SVM) classification was applied to a dataset of 400 samples, the validity of these features was demonstrated with an average AUC of 0.97 and an average accuracy of 94.40%. These results suggested that source-space functional connectivity analysis might serve as an effective indicator for detecting VRMS.

Key words： virtual reality motion sickness electroencephalogram (EEG) retrospective analysis functional connectivity analysis graph theory

Received: 29 March 2024

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R318

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	Articles by authors
	Hua Chengcheng
	Zhou Zhanfeng
	Chai Lining
	Liu Jia

Cite this article:

Hua Chengcheng,Zhou Zhanfeng,Chai Lining, et al. A Study of Motion Sickness Detection Based on Source Space Functional Connectivity Analysis[J]. Chinese Journal of Biomedical Engineering, 2025, 44(3): 267-278.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2025.03.002 OR http://cjbme.csbme.org/EN/Y2025/V44/I3/267

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