Open AccessA Pilot Study on Electroencephalogram-based Evaluation of Visually Induced Motion Sickness
Author(s) -
Ran Liu,
Miao Xu,
Yanzhen Zhang,
Eli Peli,
Alex D. Hwang
Publication year - 2020
Publication title -
the journal of imaging science and technology/the journal of imaging science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.157
H-Index - 42
eISSN - 1943-3522
pISSN - 1062-3701
DOI - 10.2352/j.imagingsci.technol.2020.64.2.020501
Subject(s) - motion sickness , electroencephalography , alpha (finance) , virtual reality , wearable computer , standard deviation , computer science , simulation , medicine , audiology , artificial intelligence , psychology , neuroscience , mathematics , statistics , embedded system , cronbach's alpha , psychometrics , clinical psychology , radiology
The most prominent problem in virtual reality (VR) technology is that users may experience motion sickness-like symptoms when they immerse into a VR environment. These symptoms are recognized as visually induced motion sickness (VIMS) or virtual reality motion sickness (VRMS). The objectives of this study were to investigate the association between the electroencephalogram (EEG) and subjectively rated VIMS level (VIMSL) and find the EEG markers for VIMS evaluation. In this study, a VR-based vehicle-driving simulator (VDS) was used to induce VIMS symptoms, and a wearable EEG device with four electrodes, the Muse, was used to collect EEG data of subjects. Our results suggest that individual tolerance, susceptibility, and recoverability to VIMS varied largely among subjects; the following markers were shown to be significantly different from no-VIMS and VIMS states (P < 0.05): (1) Means of gravity frequency (GF) for theta@FP1, alpha@TP9, alpha@FP2, alpha@TP10, and beta@FP1; (2) Standard deviation of GF for alpha@TP9, alpha@FP1, alpha@FP2, alpha@TP10, and alpha@(FP2-FP1); (3) Standard deviation of power spectral entropy (PSE) for FP1; (4) Means of Kolmogorov complexity (KC) for TP9, FP1, and FP2. These results also demonstrate that it is feasible to perform VIMS evaluation using an EEG device with a small number of electrodes.