基于变分模态分解的休息态虚拟现实晕动症脑电自动检测

Automatic EEG detection of virtual reality motion sickness in resting state based on variational mode decomposition

虚拟现实晕动症的存在是制约VR技术行业进一步发展的关键因素,研究虚拟现实晕动症相关的神经活动及对其准确检测是解决此问题的前提,此前研究缺少对休息态虚拟现实晕动症神经活动的研究。因此,本研究利用虚拟现实晕动症暴露任务前后休息态脑电信号,提出虚拟现实晕动症脑电特征作为指标实现对虚拟现实晕动症的检测。首先,通过统计分析对所选的5个电极即Fp1、Fp2、F8、T7及T8的脑电信号分别进行变分模态分解,并从选中的模态分量中提取样本熵、排列熵及中心频率。然后,通过统计检验和ReliefF算法进行两个阶段的特征选择。最后,将选择的特征向量送入支持向量机中进行分类,进而实现对虚拟现实晕动症的自动检测。结果表明,此方法准确率、灵敏度及特异度分别达到了98.3%、98.5%及98.1%,ROC曲线下的面积值达到了1,优于其他方法,证明了此方法在虚拟现实晕动症脑电信号自动检测方面优势与有效性。

The existence of virtual reality motion sickness is a key factor restricting the further development of the VR technology industry,the study of neural activity related to virtual reality motion sickness and its accurate detection is the premise to solve this problem,neural activity in resting-state virtual reality motion sickness missing from previous studies.Therefore,this study uses the resting Electroencephalogram(EEG)signals before and after the virtual reality motion sickness exposure task,and proposes the virtual reality motion sickness EEG characteristics as indicators to realize the detection of virtual reality motion sickness.First,the variational mode decomposition is performed on the EEG signals of five electrodes selected by statistical analysis in this paper,namely Fp1,Fp2,F8,T7 and T8,and extract the sample entropy,permutation entropy and center frequency from the selected modal components.Then,two stages of feature selection are performed by statistical tests and ReliefF algorithm.Finally,the selected feature vectors are sent to the support vector machine for classification,then the automatic detection of motion sickness in virtual reality is realized.The results showed that the accuracy,sensitivity and specificity of this method reached 98.3%,98.5% and 98.1%,respectively,and the area under the ROC curve reached 1,it is superior to other methods,which proves the advantages and effectiveness of this method in the automatic detection of EEG signals in virtual reality motion sickness.