Research On EEG Detection And Evaluation Method Of VR Motion Sickness

Recently,the scale of virtual reality(VR)industry has grown.However,during the VR experience,people often experience discomfort such as dizziness,nausea,and blurred eyes.This is called VR motion sickness.Studying the assessment of VR motion sickness level can effectively promote the development of VR industry.This paper proposed a subjective VR motion sickness level assessment method based on standard nausea scores,and it was compared with the traditional assessment method based on motion sickness susceptibility questionnaire(MSSQ)and motion sickness assessment questionnaire(MSAQ).Meanwhile,the effect of VR scene on the subject's VR motion sickness symptoms was studied.Based on this,the wavelet packet transform(WPT),was used to propose a feature extraction method for EEG rhythm energy ratios of delta(?),theta(?),alpha(?),and beta(?)in this research.Moreover,VR motion sickness was recognized by combining K-Nearest Neighbor classifier(KNN),support vector machine(SVM)with polynomial kernel and radial basis function kernel(RBF-SVM),respectively.First,this paper used Unreal Engine 4 to design two VR scenarios with different character movement speeds,environmental contents,and road types.The experience time,the standard nausea score,and the response time difference of 26 subjects were collected.Meanwhile,the VR motion sickness level of subjects was assessed using traditional MSSQ and MSAQ methods.Then,a VR motion sickness level assessment method based on the standard nausea score was proposed.An independent two-sample t-test was used to analyze whether different VR scenarios had significant effects on subjects' riding time,standard nausea score,and reaction time difference.Pearson and Spearman correlation coefficients were used to explain the relationship between subjects' riding time,standard nausea score,response time difference,total MSSQ score,gastrointestinal discomfort score,eye discomfort score,disorientation score,and total MSAQ score.Next,based on the newly proposed VR motion sickness level assessment method for the standard nausea score,the Openbci system was used to record EEG signals from 24 subjects under normal,moderate,and severe VR motion sickness.Finally,EEG signals were used to recognize VR motion sickness level.First,the original EEG signal was filtered by elliptical filter.Next,based on wavelet packet transformation,a new EEG feature extraction method based on delta,theta,alpha,and beta rhythmic energy ratios was proposed.Then,combined with KNN,polynomial-SVM and RBF-SVM,the EEG signal was used to recognize the two and three levels VR motion sickness.Finally,the classification results were evaluated and compared with other similar research.The experimental results show that,compared with the traditional method,the newly proposed VR motion sickness level assessment method based on standard nausea score has the characteristics of less feedback to the subjects,and is basically not affected by the environment and memory lag.Compared with other methods,the newly proposed rhythmic wavelet packet energy ratio feature extraction method,combined with KNN,polynomial-SVM and RBF-SVM,has a higher recognition accuracy rate for VR motion sickness level of single subject and multiple subjects,reaching 98.25% and 79.25%,respectively.