Comparing Autonomic Physiological and Electroencephalography Features for VR Sickness Detection Using Predictive Models

Gang Li; Ogechi  Onuoha; Mark  Mcgill; Stephen Anthony Brewster; Chao Ping Chen; Frank Pollick

doi:10.1109/ssci50451.2021.9660126

Comparing Autonomic Physiological and Electroencephalography Features for VR Sickness Detection Using Predictive Models

Gang Li, Ogechi Onuoha, Mark Mcgill, Stephen Anthony Brewster, Chao Ping Chen, Frank Pollick

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

8 Citations (SciVal)

Abstract

How the performance of autonomic physiological, and human vestibular network (HVN)-based brain functional connectivity (BFC) features differ in a virtual reality (VR) sickness classification task is underexplored. Therefore, this paper presents an artificial intelligence (AI)-aided comparative study of the two. Results from different AI models all show that autonomic physiological features represented by the combined heart rate, fingertip temperature and forehead temperature are superior to HVN-based BFC features represented by the phase-locking values of inter-electrode coherence (IEC) of electroencephalogram (EEG) in the same VR sickness condition (that is, as a result of experiencing tunnel travel-induced illusory self-motion (vection) about moving in-depth in this study). Regarding EEG features per se (IEC-BFC vs traditional power spectrum), we did not find much difference across AI models.

Original language	English
Title of host publication	2021 IEEE Symposium Series on Computational Intelligence (SSCI)
Publisher	IEEE
ISBN (Electronic)	9781728190495
ISBN (Print)	9781728190488
DOIs	https://doi.org/10.1109/ssci50451.2021.9660126
Publication status	Published - 24 Jan 2022

Publication series

Name	IEEE Symposium Series on Computational Intelligence (SSCI)
Publisher	IEEE

Funding

This research is sponsored by European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (No. 835197) and National Natural Science Foundation of China (No. 61901264) (Corresponding Author: Gang Li).

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10.1109/ssci50451.2021.9660126

https://eprints.gla.ac.uk/256772/

Cite this

Li, G., Onuoha, O., Mcgill, M., Brewster, S. A., Chen, C. P., & Pollick, F. (2022). Comparing Autonomic Physiological and Electroencephalography Features for VR Sickness Detection Using Predictive Models. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (IEEE Symposium Series on Computational Intelligence (SSCI)). IEEE. https://doi.org/10.1109/ssci50451.2021.9660126

Comparing Autonomic Physiological and Electroencephalography Features for VR Sickness Detection Using Predictive Models. / Li, Gang; Onuoha, Ogechi; Mcgill, Mark et al.
2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2022. (IEEE Symposium Series on Computational Intelligence (SSCI)).

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

Li, G, Onuoha, O, Mcgill, M, Brewster, SA, Chen, CP & Pollick, F 2022, Comparing Autonomic Physiological and Electroencephalography Features for VR Sickness Detection Using Predictive Models. in 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE Symposium Series on Computational Intelligence (SSCI), IEEE. https://doi.org/10.1109/ssci50451.2021.9660126

Li G, Onuoha O, Mcgill M, Brewster SA, Chen CP, Pollick F. Comparing Autonomic Physiological and Electroencephalography Features for VR Sickness Detection Using Predictive Models. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE. 2022. (IEEE Symposium Series on Computational Intelligence (SSCI)). Epub 2022 Jan 24. doi: 10.1109/ssci50451.2021.9660126

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Comparing Autonomic Physiological and Electroencephalography Features for VR Sickness Detection Using Predictive Models

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