Feature extraction method of EEG based on wavelet packet reconstruction and deep learning model of VR motion sickness feature classification and prediction
Open accessAbstract
The surging popularity of virtual reality (VR) technology raises concerns about VR-induced motion sickness, linked to discomfort and nausea in simulated environments. Our method involves in-depth analysis of EEG data and user feedback to train a sophisticated deep learning model, utilizing an enhanced GRU network for identifying motion sickness patterns. Following comprehensive data pre-processing and feature engineering to ensure input accuracy, a deep learning model is trained using supervised and unsupervised techniques for classifying and predicting motion sickness severity. Rigorous training and validation procedures confirm the model's robustness across diverse scenarios. Research results affirm our deep learning model's 84.9% accuracy in classifying and predicting VR-induced motion sickness, surpassing existing models. This information is vital for improving the VR experience and advancing VR technology.
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Exposure therapy is seldom provided.
“Considered a leading standard treatment for anxiety and related disorders [1], exposure therapy is nonetheless seldom provided [23].”
From Exploring Predictors of Counselors’ Acceptance of Virtual Reality Exposure Therapy With Resistance and Job Contexts as Moderators: Cross-Sectional Mixed Methods Study (2025) · cites this paper as [23]
The Dataset and methods section (2.1; page unavailable) describes a VR motion-sickness experiment using EEG, not the provision of exposure therapy. Searches for exposure therapy and its use in treating anxiety disorders found no passage addressing how often it is provided. (LLM, conf=0.77)
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References 30 · Cited by 10
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- Authors
- Luo, S., Ren, P., Wu, J., Wu, X., & Zhang, X.
- Affiliations
- Xuzhou Medical College (CN)
- Corresponding author
- Ren, Peng
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2024-07-19
- Volume
- 19
- Issue
- 7
- Pages
- e0305733
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC11259303
- OpenAlex
- W4400811709
- Cited by
- 10
- References
- 30
- Citations by year
- 2026: 8 · 2025: 1 · 2024: 1
- FWCI
- 2.43
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- The Unveiling & Leading Project of XZHMU (JBGS202204)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Virtual Reality Applications and Impacts
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- virtual reality motion sickness, EEG feature extraction, user feedback, nausea
- MeSH terms
- Virtual Reality, Deep Learning, Adult, Electroencephalography, Female, Humans, Male, Motion Sickness, Young Adult, Wavelet Analysis
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