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

Gang Li; Haerin 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

Gang Li
, Haerin Byeon
, Sung-Bae Cho
, Frank Pollick

Yonsei University
University of Glasgow

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

2 Citations (SciVal)

77 Downloads (Pure)

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.

Original language	English
Title of host publication	2025 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)
Publisher	IEEE
Pages	171-175
Number of pages	5
ISBN (Electronic)	979-8-3315-2157-8
ISBN (Print)	979-8-3315-2158-5
DOIs	https://doi.org/10.1109/AIxVR63409.2025.00033
Publication status	Published - 26 Feb 2025
Event	the 7th IEEE International Conference on Artificial Intelligence & eXtended and Virtual Reality - Instituto Superior Técnico, Lisbon, Portugal Duration: 27 Jan 2025 → 29 Jan 2025

Publication series

Name
ISSN (Electronic)	2771-7445

Conference

Conference	the 7th IEEE International Conference on Artificial Intelligence & eXtended and Virtual Reality
Abbreviated title	IEEE AIxVR
Country/Territory	Portugal
City	Lisbon
Period	27/01/25 → 29/01/25

Keywords

EEG
VR cybersickness
machine learning

ASJC Scopus subject areas

Artificial Intelligence
Media Technology
Modelling and Simulation

Access to Document

10.1109/AIxVR63409.2025.00033

conference-template-a4_ver1.6
This is the accepted version of the paper published in conference proceedings by IEEE at: 10.1109/AIxVR63409.2025.00033
Accepted author manuscript, 376 KB

Cite this

VR Cybersickness Classification Using Machine Learning Models on Open-Access EEG Datasets from the Human Vestibular Network. / Li, Gang; Haerin Byeon; Sung-Bae Cho et al.
2025 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR). IEEE, 2025. p. 171-175.

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

Li, G, Haerin Byeon, Sung-Bae Cho & Frank Pollick 2025, VR Cybersickness Classification Using Machine Learning Models on Open-Access EEG Datasets from the Human Vestibular Network. in 2025 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR). IEEE, pp. 171-175, the 7th IEEE International Conference on Artificial Intelligence & eXtended and Virtual Reality, Lisbon, Portugal, 27/01/25. https://doi.org/10.1109/AIxVR63409.2025.00033

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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.",

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AB - 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.

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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