Abstract：
The existence of Virtual Reality Motion Sickness (VRMS) is a key factor restricting the further development of the VR industry, and the premise to solve this problem is to be able to accurately and effectively detect its occurrence. In view of the current lack of high-accuracy and effective detection methods, this paper proposes a VRMS detection method based on entropy asymmetry and cross-frequency coupling value asymmetry of EEG. First of all, the EEG of the four selected pairs of electrodes on the bilateral brain are subjected to Multivariate Variational Mode Decomposition (MVMD) respectively, and three types of entropy values on the low-frequency and high-frequency components are calculated, namely approximate entropy, fuzzy entropy and permutation entropy, as well as three types of phase-amplitude coupling features between the low-frequency and high-frequency components, namely the mean value, standard deviation and correlation coefficient; Secondly, the difference of the entropies and the cross-frequency coupling features between the left electrodes and the right electrodes are calculated; Finally, the final feature set are selected via t-test and fed into the SVM for classification, thus realizing the automatic detection of VRMS. The results show that the three classification indexes under this method, i.e., accuracy, sensitivity and specificity, reach 99.5 %, 99.3 % and 99.7 %, respectively, and the value of the area under the ROC curve reached 1, which proves that this method can be an effective indicator for detecting the occurrence of VRMS.
摘要：
虚拟现实疾病(VRMS)的存在是制约VR产业进一步发展的关键因素,而解决这个问题的前提是能够准确有效地检测到它的发生。针对目前缺乏高精度、有效的检测方法,提出了一种基于熵不对称性和交叉频率耦合值不对称性的脑电信号VRMS检测方法。首先,分别对所选的4对双侧大脑电极的EEG进行多变量变分模态分解(MVMD),并计算了低频和高频分量上的三种熵值，即近似熵，模糊熵和排列熵，以及低频和高频分量之间的三种相幅耦合特征，即平均值，标准差和相关系数；其次，计算了左电极和右电极之间的熵差和交叉频率耦合特征；最后，通过t检验选择最终的特征集，并将其输入SVM进行分类，从而实现了VRMS的自动检测。结果表明,该方法下的三个分类指标,即，准确度,敏感性和特异性，达到99.5%,99.3%和99.7%，分别,ROC曲线下面积值达到1,证明该方法可作为检测VRMS发生的有效指标。
