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Assessment of 3D Visual Discomfort Based on Dynamic Functional Connectivity Analysis with HMM in EEG
Stereoscopic displays can induce visual discomfort despite their wide application. Electroencephalography (EEG) technology has been applied to assess 3D visual discomfort, because it can capture brain activities with high temporal resolution. Previous studies explored the frequency and temporal feat...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313185/ https://www.ncbi.nlm.nih.gov/pubmed/35884743 http://dx.doi.org/10.3390/brainsci12070937 |
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author | Long, Zhiying Liu, Lu Yuan, Xuefeng Zheng, Yawen Niu, Yantong Yao, Li |
author_facet | Long, Zhiying Liu, Lu Yuan, Xuefeng Zheng, Yawen Niu, Yantong Yao, Li |
author_sort | Long, Zhiying |
collection | PubMed |
description | Stereoscopic displays can induce visual discomfort despite their wide application. Electroencephalography (EEG) technology has been applied to assess 3D visual discomfort, because it can capture brain activities with high temporal resolution. Previous studies explored the frequency and temporal features relevant to visual discomfort in EEG data. Recently, it was demonstrated that functional connectivity between brain regions fluctuates with time. However, the relationship between 3D visual discomfort and dynamic functional connectivity (DFC) remains unknown. Although HMM showed advantages over the sliding window method in capturing the temporal fluctuations of DFC at a single time point in functional magnetic resonance imaging (fMRI) data, it is unclear whether HMM works well in revealing the time-varying functional connectivity of EEG data. In this study, the hidden Markov model (HMM) was introduced to DFC analysis of EEG data for the first time and was used to investigate the DFC features that can be used to assess 3D visual discomfort. The results indicated that state 2, with strong connections between electrodes, occurred more frequently in the early period, whereas state 4, with overall weak connections between electrodes, occurred more frequently in the late period for both visual comfort and discomfort stimuli. Moreover, the 3D visual discomfort stimuli caused subjects to stay in state 4 more frequently, especially in the later period, in contrast to the 3D visual comfort stimuli. The results suggest that the increasing occurrence of state 4 was possibly related to visual discomfort and that the occurrence frequency of state 4 may be used to assess visual discomfort. |
format | Online Article Text |
id | pubmed-9313185 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93131852022-07-26 Assessment of 3D Visual Discomfort Based on Dynamic Functional Connectivity Analysis with HMM in EEG Long, Zhiying Liu, Lu Yuan, Xuefeng Zheng, Yawen Niu, Yantong Yao, Li Brain Sci Article Stereoscopic displays can induce visual discomfort despite their wide application. Electroencephalography (EEG) technology has been applied to assess 3D visual discomfort, because it can capture brain activities with high temporal resolution. Previous studies explored the frequency and temporal features relevant to visual discomfort in EEG data. Recently, it was demonstrated that functional connectivity between brain regions fluctuates with time. However, the relationship between 3D visual discomfort and dynamic functional connectivity (DFC) remains unknown. Although HMM showed advantages over the sliding window method in capturing the temporal fluctuations of DFC at a single time point in functional magnetic resonance imaging (fMRI) data, it is unclear whether HMM works well in revealing the time-varying functional connectivity of EEG data. In this study, the hidden Markov model (HMM) was introduced to DFC analysis of EEG data for the first time and was used to investigate the DFC features that can be used to assess 3D visual discomfort. The results indicated that state 2, with strong connections between electrodes, occurred more frequently in the early period, whereas state 4, with overall weak connections between electrodes, occurred more frequently in the late period for both visual comfort and discomfort stimuli. Moreover, the 3D visual discomfort stimuli caused subjects to stay in state 4 more frequently, especially in the later period, in contrast to the 3D visual comfort stimuli. The results suggest that the increasing occurrence of state 4 was possibly related to visual discomfort and that the occurrence frequency of state 4 may be used to assess visual discomfort. MDPI 2022-07-18 /pmc/articles/PMC9313185/ /pubmed/35884743 http://dx.doi.org/10.3390/brainsci12070937 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Long, Zhiying Liu, Lu Yuan, Xuefeng Zheng, Yawen Niu, Yantong Yao, Li Assessment of 3D Visual Discomfort Based on Dynamic Functional Connectivity Analysis with HMM in EEG |
title | Assessment of 3D Visual Discomfort Based on Dynamic Functional Connectivity Analysis with HMM in EEG |
title_full | Assessment of 3D Visual Discomfort Based on Dynamic Functional Connectivity Analysis with HMM in EEG |
title_fullStr | Assessment of 3D Visual Discomfort Based on Dynamic Functional Connectivity Analysis with HMM in EEG |
title_full_unstemmed | Assessment of 3D Visual Discomfort Based on Dynamic Functional Connectivity Analysis with HMM in EEG |
title_short | Assessment of 3D Visual Discomfort Based on Dynamic Functional Connectivity Analysis with HMM in EEG |
title_sort | assessment of 3d visual discomfort based on dynamic functional connectivity analysis with hmm in eeg |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313185/ https://www.ncbi.nlm.nih.gov/pubmed/35884743 http://dx.doi.org/10.3390/brainsci12070937 |
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