基于脑电节律能量与模糊熵的VR诱发晕动症水平检测研究

TP391; 晕动症一直是影响虚拟现实用户体验及限制虚拟现实行业发展的一个关键因素.为解决这一问题,本文研究了虚拟现实晕动症对大脑神经活动的影响,并利用脑电特征对晕动症水平进行检测.为得到可度量眩晕水平的特征,记录受试者在体验眩晕测试场景前及过程中的脑电信号,计算节律能量和模糊熵,并利用统计分析进行特征选择,最后分类验证该特征的有效性.结果表明,受试者产生晕动症时,CP4和Oz的θ、α频段能量及C4的β、γ频段能量显著降低(p<0.01);在模糊熵方面,δ频段有FC4、Cz模糊熵值显著升高(p<0.000 1),β频段有O1模糊熵值显著降低(p<0.000 1).对比线性判别...

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Published in	数据采集与处理 Vol. 39; no. 2; pp. 490 - 500
Main Authors	周占峰, 化成城, 柴立宁, 严颖, 刘佳, 付荣荣
Format	Journal Article
Language	Chinese
Published	南京信息工程大学江苏省智能气象探测机器人工程研究中心,南京 210044 01.03.2024 南京信息工程大学自动化学院,南京 210044 南京信息工程大学江苏省大气环境与装备技术协同创新中心,南京 210044%南京信息工程大学自动化学院,南京 210044%燕山大学河北省测试计量技术及仪器重点实验室,秦皇岛 066000
Subjects	electroencephalogram(EEG)signal 模糊熵 virtual reality 虚拟现实脑电信号 fuzzy entropy motion sickness 晕动症
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Abstract	TP391; 晕动症一直是影响虚拟现实用户体验及限制虚拟现实行业发展的一个关键因素.为解决这一问题,本文研究了虚拟现实晕动症对大脑神经活动的影响,并利用脑电特征对晕动症水平进行检测.为得到可度量眩晕水平的特征,记录受试者在体验眩晕测试场景前及过程中的脑电信号,计算节律能量和模糊熵,并利用统计分析进行特征选择,最后分类验证该特征的有效性.结果表明,受试者产生晕动症时,CP4和Oz的θ、α频段能量及C4的β、γ频段能量显著降低(p<0.01);在模糊熵方面,δ频段有FC4、Cz模糊熵值显著升高(p<0.000 1),β频段有O1模糊熵值显著降低(p<0.000 1).对比线性判别分析(Linear discriminant analysis,LDA)、逻辑回归(Logistic regression,LR)和支持向量机(Support vector machine,SVM),K最近邻(K-nearest neighbor,KNN)算法的分类效果较好,它在节律能量和模糊熵上的分类准确率分别为89%和91%.本研究表明脑电节律能量及模糊熵有望成为晕动症水平检测的有效指标,为研究虚拟现实晕动症成因及缓解方案提供客观依据.
AbstractList	TP391; 晕动症一直是影响虚拟现实用户体验及限制虚拟现实行业发展的一个关键因素.为解决这一问题,本文研究了虚拟现实晕动症对大脑神经活动的影响,并利用脑电特征对晕动症水平进行检测.为得到可度量眩晕水平的特征,记录受试者在体验眩晕测试场景前及过程中的脑电信号,计算节律能量和模糊熵,并利用统计分析进行特征选择,最后分类验证该特征的有效性.结果表明,受试者产生晕动症时,CP4和Oz的θ、α频段能量及C4的β、γ频段能量显著降低(p<0.01);在模糊熵方面,δ频段有FC4、Cz模糊熵值显著升高(p<0.000 1),β频段有O1模糊熵值显著降低(p<0.000 1).对比线性判别分析(Linear discriminant analysis,LDA)、逻辑回归(Logistic regression,LR)和支持向量机(Support vector machine,SVM),K最近邻(K-nearest neighbor,KNN)算法的分类效果较好,它在节律能量和模糊熵上的分类准确率分别为89%和91%.本研究表明脑电节律能量及模糊熵有望成为晕动症水平检测的有效指标,为研究虚拟现实晕动症成因及缓解方案提供客观依据.
Abstract_FL	Motion sickness has been a key factor affecting the virtual reality user experience and limiting the growth of the virtual reality industry.To address this issue,this paper investigates the effects of virtual reality motion sickness on neural activity in the brain and uses electroencephalogram(EEG)features to detect levels of motion sickness.To obtain features that can measure the level of vertigo,this paper records the EEG signals of subjects before and during the experience of the vertigo test scene,calculates the rhythm energy and fuzzy entropy,uses statistical analysis for feature selection,and finally classifies and verifies the validity of the features.The results show that the energy in the θ and α bands of CP4 and Oz and the energy in the β and γ bands of C4 are significantly reduced when subjects develop motion sickness(p<0.01);in terms of fuzzy entropy,there are significantly higher values of FC4 and Cz fuzzy entropy in the δ band(p<0.000 1)and significantly lower values of O1 fuzzy entropy in the β band(p<0.000 1).Compared to linear discriminant analysis(LDA),logistic regression(LR)and support vector machine(SVM),K nearest neighbor(KNN)shows better classification results with 89%and 91%classification accuracy on rhythm energy and fuzzy entropy,respectively.This study shows that EEG rhythm energy and fuzzy entropy are expected to be effective indicators for motion sickness level detection,providing an objective basis for studying the causes of virtual reality motion sickness and mitigation options.
Author	化成城付荣荣刘佳严颖周占峰柴立宁
AuthorAffiliation	南京信息工程大学自动化学院,南京 210044;南京信息工程大学江苏省智能气象探测机器人工程研究中心,南京 210044;南京信息工程大学江苏省大气环境与装备技术协同创新中心,南京 210044%南京信息工程大学自动化学院,南京 210044%燕山大学河北省测试计量技术及仪器重点实验室,秦皇岛 066000
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Author_FL	HUA Chengcheng LIU Jia FU Rongrong YAN Ying ZHOU Zhanfeng CHAI Lining
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Title	基于脑电节律能量与模糊熵的VR诱发晕动症水平检测研究
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