TY - GEN
T1 - Resting-state EEG in the Vestibular Region Can Predict Motion Sickness Induced by a Motion-Simulated in-car VR Platform
AU - Li, G.
AU - Wang, Y.-K.
AU - McGill, M.
AU - Pohlmann, K.
AU - Brewster, S.
AU - Pollick, F.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - Monitoring in-car VR motion sickness (VRMS) by neurophysiological signals is a formidable challenge due to unavoidable motion artifacts caused by the moving vehicle and necessary physical movements by the user to interact with the VR environment. Therefore, this paper for the first time investigates if resting-state neurophysiological features and self-reports of stress levels collected prior to exposure to a motion-simulated in-car VRMS induction platform could predict final motion sickness ratings. Our results of linear regression modeling show that the traditional EEG power spectrum was the only resting-state feature set that could predict in-car VRMS ratings. Further, the best regression result was achieved by beta power spectrum in the left parietal area with adjusted R2=22.6% versus 11.6% in the right. This result not only confirmed the left parietal involvement in motion sickness susceptibility observed in a previous resting-state fMRI study, but also advanced that methodology to mobile neurotechnologies, represented by mobile EEG, referenced by other types of resting-state features. Together, this study may offer a new mobile neurotechnology-based approach to predict passengers' VRMS levels before they start to use VR apps in a moving vehicle.
AB - Monitoring in-car VR motion sickness (VRMS) by neurophysiological signals is a formidable challenge due to unavoidable motion artifacts caused by the moving vehicle and necessary physical movements by the user to interact with the VR environment. Therefore, this paper for the first time investigates if resting-state neurophysiological features and self-reports of stress levels collected prior to exposure to a motion-simulated in-car VRMS induction platform could predict final motion sickness ratings. Our results of linear regression modeling show that the traditional EEG power spectrum was the only resting-state feature set that could predict in-car VRMS ratings. Further, the best regression result was achieved by beta power spectrum in the left parietal area with adjusted R2=22.6% versus 11.6% in the right. This result not only confirmed the left parietal involvement in motion sickness susceptibility observed in a previous resting-state fMRI study, but also advanced that methodology to mobile neurotechnologies, represented by mobile EEG, referenced by other types of resting-state features. Together, this study may offer a new mobile neurotechnology-based approach to predict passengers' VRMS levels before they start to use VR apps in a moving vehicle.
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85182918846&partnerID=MN8TOARS
U2 - 10.1109/SSCI52147.2023.10371953
DO - 10.1109/SSCI52147.2023.10371953
M3 - Chapter in a published conference proceeding
SN - 9781665430654
BT - 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023
PB - IEEE
ER -