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

Li, G. , Onuoha, O., McGill, M., Brewster, S. , Chen, C. P. and Pollick, F. (2021) Comparing Autonomic Physiological and Electroencephalography Features for VR Sickness Detection Using Predictive Models. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, FL, USA, 04-07 Dec 2021, ISBN 9781728190488 (doi: 10.1109/SSCI50451.2021.9660126)

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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.

Item Type:Conference Proceedings
Additional Information: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).
Glasgow Author(s) Enlighten ID:Pollick, Professor Frank and Brewster, Professor Stephen and Onuoha, Ogechi and Li, Dr Gang and McGill, Dr Mark
Authors: Li, G., Onuoha, O., McGill, M., Brewster, S., Chen, C. P., and Pollick, F.
College/School:College of Science and Engineering > School of Computing Science
College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Research Group:Multimodal Interaction Group
Published Online:24 January 2022
First Published:First published in
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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