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Machine Learning-Guided High-Definition Transcranial Direct Current Stimulation Prevents Cybersickness

aut.relation.articlenumber94
aut.relation.issue3
aut.relation.journalVirtual Reality
aut.relation.startpage94
aut.relation.volume29
dc.contributor.authorYang, AHX
dc.contributor.authorGalán-Augé, C
dc.contributor.authorKasabov, NK
dc.contributor.authorCakmak, YO
dc.date.accessioned2025-06-26T22:44:55Z
dc.date.available2025-06-26T22:44:55Z
dc.date.issued2025-06-10
dc.description.abstractExtended reality (XR) environments, such as simulators, augmented reality, and virtual reality are major techniques in contemporary AI and entertainment systems. Cybersickness (CS) is a motion-sickness experienced by many users of XR. CS causes debilitating nausea, disorientation, and oculomotor issues. Treatment and prevention for motion-sickness typically involves drugs with sedative properties that impair task performance. These drugs are non-specific to CS and counter intuitive for enabling activity within XR. Our paper finds that there are specific spatiotemporal patterns of brain activity in certain functional networks related to CS and offers a method for the analysis of these patterns. The method can predict CS ahead of its onset and most importantly it suggests what intervention to apply in order to prevent CS in individuals. We apply a novel approach to CS prevention by using our previously developed spiking neural network (SNN) method, which can predict CS using electroencephalogram (EEG) pre-VR usage, before applying neuromodulation to disrupt CS-related functional networks in the brain. This approach provides an additional layer of screening before intervention with high-definition transcranial direct current stimulation (HD-tDCS). The study recruited healthy CS susceptible participants (9 male, 10 female, n = 19, 18–36 years old) and used a within-subjects design. EEG (32-channel, 10–10-configuration) was monitored at seated-rest and processed through the SNN for CS prediction. Immediately following a positive prediction, either sham, anodal or cathodal HD-tDCS was applied at the Cz area (5-min, 1.5 mA, 30 s-ramp-up/down) with subsequent 10-min VR immersion to record CS events. Main results: Cathodal stimulation yielded a significantly higher number of successful preventions compared to anodal (*p = 0.01) and sham (***p = 0.00056), achieving a large effect size (> 0.8) with a 47% reduction in CS likelihood. Significance: The treatment was hypothesized to work through disruption of activity at the motor processing and planning regions under Cz. The area appears to be a marker of ongoing CS susceptibility, and also a contributor towards the condition.
dc.identifier.citationVirtual Reality, ISSN: 1359-4338 (Print); 1434-9957 (Online), Springer Science and Business Media LLC, 29(3), 94-. doi: 10.1007/s10055-025-01160-x
dc.identifier.doi10.1007/s10055-025-01160-x
dc.identifier.issn1359-4338
dc.identifier.issn1434-9957
dc.identifier.urihttp://hdl.handle.net/10292/19382
dc.languageen
dc.publisherSpringer Science and Business Media LLC
dc.relation.urihttps://link.springer.com/article/10.1007/s10055-025-01160-x
dc.rightsOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.accessrightsOpenAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject46 Information and Computing Sciences
dc.subject4608 Human-Centred Computing
dc.subjectNetworking and Information Technology R&D (NITRD)
dc.subjectBioengineering
dc.subjectNeurosciences
dc.subjectPrevention
dc.subjectClinical Research
dc.subject0801 Artificial Intelligence and Image Processing
dc.subject0909 Geomatic Engineering
dc.subject1702 Cognitive Sciences
dc.subjectHuman Factors
dc.subject4607 Graphics, augmented reality and games
dc.titleMachine Learning-Guided High-Definition Transcranial Direct Current Stimulation Prevents Cybersickness
dc.typeJournal Article
pubs.elements-id611007

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