|
|
|
|
| LEADER |
01000naa a22002652c 4500 |
| 001 |
NLM393680479 |
| 003 |
DE-627 |
| 005 |
20251007232633.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
251007s2025 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TVCG.2025.3616797
|2 doi
|
| 028 |
5 |
2 |
|a pubmed25n1592.xml
|
| 035 |
|
|
|a (DE-627)NLM393680479
|
| 035 |
|
|
|a (NLM)41052115
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Wang, Peike
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Multimodal Contrastive Learning for Cybersickness Recognition Using Brain Connectivity Graph Representation
|
| 264 |
|
1 |
|c 2025
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 06.10.2025
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status Publisher
|
| 520 |
|
|
|a Cybersickness significantly impairs user comfort and immersion in virtual reality (VR). Effective identification of cybersickness leveraging physiological, visual, and motion data is a critical prerequisite for its mitigation. However, current methods primarily employ direct feature fusion across modalities, which often leads to limited accuracy due to inadequate modeling of inter-modal relationships. In this paper, we propose a multimodal contrastive learning method for cybersickness recognition. First, we introduce Brain Connectivity Graph Representation (BCGR), an innovative graph-based representation that captures cybersickness-related connectivity patterns across modalities. We further develop three BCGR instances: E-BCGR, constructed based on EEG signals; MV-BCGR, constructed based on video and motion data; and S-BCGR, obtained through our proposed standardized decomposition algorithm. Then, we propose a connectivity-constrained contrastive fusion module, which aligns E-BCGR and MV-BCGR into a shared latent space via graph contrastive learning while utilizing S-BCGR as a connectivity constraint to enhance representation quality. Moreover, we construct a multimodal cybersickness dataset comprising synchronized EEG, video, and motion data collected in VR environments to promote further research in this domain. Experimental results demonstrate that our method outperforms existing state-of-the-art methods across four critical evaluation metrics: accuracy, sensitivity, specificity, and the area under the curve. Source code: https://github.com/PEKEW/cybersickness-bcgr
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Li, Ming
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Wang, Ziteng
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Liu, Yong-Jin
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Wang, Lili
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g PP(2025) vom: 06. Okt.
|w (DE-627)NLM098269445
|x 1941-0506
|7 nnas
|
| 773 |
1 |
8 |
|g volume:PP
|g year:2025
|g day:06
|g month:10
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TVCG.2025.3616797
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d PP
|j 2025
|b 06
|c 10
|