Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ University of Bath's...arrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
University of Bath's research portal
Conference object . 2025
Data sources: University of Bath's research portal
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
https://doi.org/10.1109/aixvr6...
Article . 2025 . Peer-reviewed
License: STM Policy #29
Data sources: Crossref
 View all 2 versions
Claim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

VR Cybersickness Classification using Machine Learning Models on Open-Access EEG Datasets from the Human Vestibular Network

descriptionPublicationkeyboard_double_arrow_right Article , Conference object 27 Jan 2025Publisher:IEEEJournal:2025 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)
Authors: Gang Li; Hae-Rin Byeon; Sung-Bae Cho; Frank Pollick;

doi: 10.1109/aixvr63409.2025.00033

VR Cybersickness Classification using Machine Learning Models on Open-Access EEG Datasets from the Human Vestibular Network

Abstract

This paper addresses key challenges in EEG-based cybersickness classification using machine learning (ML) models. Despite significant research in this area four critical issues remain unresolved: 1) the availability of open-access EEG datasets; 2) imbalanced data distribution; 3) limited generalizability testing, and 4) insufficient exploration of personalized EEG data.

Related Organizations
Keywords

/dk/atira/pure/subjectarea/asjc/1700/1702; name=Artificial Intelligence, /dk/atira/pure/subjectarea/asjc/2200/2214; name=Media Technology, machine learning, VR cybersickness, EEG, /dk/atira/pure/subjectarea/asjc/2600/2611; name=Modelling and Simulation

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    		 Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    		 Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
Related to Research communities
UArctic