Electroencephalogram-Based Satisfaction Assessment Brain–Computer Interface in Emerging Video Service by Using Graph Representation Learning
We validate the effectiveness of introducing a P2G learning strategy in EVS satisfaction modeling using a self-constructed dataset and a relevant public dataset, and our method outperforms existing methods. Additionally, we provide a detailed visual analysis to unveil neural markers associated with EVS satisfaction, thereby laying a scientific foundation for the optimization and development of video services.
Authors
- Yifan Niu
- Xia Wu (ORCID: https://orcid.org/0000-0002-2377-6093)
- Gangyan Zeng (ORCID: https://orcid.org/0000-0003-2696-8549)
- Ziyu Li (ORCID: https://orcid.org/0000-0002-9443-7689)
- Yuan Zhang (ORCID: https://orcid.org/0000-0002-1335-2780)
- Li Yao (ORCID: https://orcid.org/0000-0001-7730-7850)
Institutions
- Beijing Institute of Technology (CN)
- Beijing Normal University (CN)
- Nanjing University of Science and Technology (CN)
- Communication University of China (CN)
Publication Details
- Journal
- Brain Connectivity
- Published
- 2025-09-26
- DOI
- https://doi.org/10.1177/21580014251359107
- Citations
- 1
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 1.43