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.