Evaluating 3D Visual Fatigue Induced by VR Headset Using EEG and Self-attention CNN

Haochen Hu*, Yue Liu, Kang Yue

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

3D visual fatigue is one of the major factors that hinder the development of virtual reality contents towards larger population. We proposed an EEG-based self-attention CNN model to evaluate user's 3D visual fatigue in an end-to-end fashion. We adopted a wavelet-based convolution to extract spatiotemporal information and prevent overfitting. Besides, a self-attention layer was added to the feature extractor backbone to cope with the subject-variation problem in EEG-decoding. The proposed method is compared with four state-of-the-art methods, and the results demonstrate that our model has the best performance among all methods in subject-dependent and cross-subject scenarios.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages784-785
Number of pages2
ISBN (Electronic)9781665484022
DOIs
Publication statusPublished - 2022
Event2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022 - Virtual, Online, New Zealand
Duration: 12 Mar 202216 Mar 2022

Publication series

NameProceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022

Conference

Conference2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022
Country/TerritoryNew Zealand
CityVirtual, Online
Period12/03/2216/03/22

Keywords

  • HCI design and evaluation methods
  • Human computer interaction (HCI)
  • Human-centered computing
  • People with disabilities
  • Social and professional topics
  • User characteristics
  • User studies

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