Multimodal biosensing for vestibular network-based cybersickness detection

Li, G. , McGill, M., Brewster, S. , Chen, C. P., Anguera, J. A., Gazzaley, A. and Pollick, F. (2022) Multimodal biosensing for vestibular network-based cybersickness detection. IEEE Journal of Biomedical and Health Informatics, 26(6), pp. 2469-2480. (doi: 10.1109/JBHI.2021.3134024)

Abstract

Virtual reality (VR) has the potential to induce cybersickness (CS), which impedes CS-susceptible VR users from the benefit of emerging VR applications. To better detect CS, the current study investigated whether/how the newly proposed human vestibular network (HVN) is involved in flagship consumer VR-induced CS by simultaneously recording autonomic physiological signals as well as neural signals generated in sensorimotor and cognitive domains. The VR stimuli were made up of one or two moderate CS-inducing entertaining task(s) as well as a mild CS-inducing cognitive task implemented before and after the moderate CS task(s). Results not only showed that CS impaired cognitive control ability, represented by the degree of attentional engagement, but also revealed that combined indicators from all three HVN domains could together establish the best regression relationship with CS ratings. More importantly, we found that every HVN domain had its unique advantage with the dynamic changes in CS severity and time. These results provide evidence for involvement of the HVN in CS and indicate the necessity of HVN-based CS detection.

• Item Type: Articles
• Additional Information: This research is sponsored by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (No. 835197) and the National Natural Science Foundation of China (No. 61901264).
• Keywords: Virtual reality, cybersickness, multimodal sensing, cognitive control ability, vestibular network.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Pollick, Professor Frank and Brewster, Professor Stephen and Li, Dr Gang and McGill, Dr Mark
• Authors: Li, G., McGill, M., Brewster, S., Chen, C. P., Anguera, J. A., Gazzaley, A., and Pollick, F.
• College/School: College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience; College of Science and Engineering > School of Computing Science
• Research Group: Multimodal Interaction Group
• Journal Name: IEEE Journal of Biomedical and Health Informatics
• Journal Abbr.: IEEE J-BHI
• Publisher: IEEE
• ISSN: 2168-2194
• ISSN (Online): 2168-2208
• Published Online: 09 December 2021
• Copyright Holders: Copyright © 2021 IEEE
• First Published: First published in IEEE Journal of Biomedical and Health Informatics 26(6): 2469-2480
• Publisher Policy: Reproduced in accordance with the publisher copyright policy