Research on decoding technology of visual brain-computer interface based on convolutional neural network
Brain-computer interface is to establish a direct communication channel between human brain and computer by reading the electrical signal of human brain activity, so as to realize the direct communication between human and computer and machine. At present, certain progress has been made in the application of brain-computer interface technology in the treatment and rehabilitation training of diseases related to movement disorders. The new machine learning technology represented by deep learning has received more and more attention in the field of brain-computer interface, especially the brain-computer interface system based on convolutional neural network can effectively solve the problem of low recognition accuracy of traditional brain-computer interface system. In this paper, the decoding technology of visual brain-computer interface based on CNN is studied.
Authors
- Kun He (ORCID: https://orcid.org/0009-0006-5036-6889)
- Yong-Guk Kim
- Yutong Zhou (ORCID: https://orcid.org/0000-0002-3518-1441)
- Kun Li (ORCID: https://orcid.org/0000-0003-2012-2593)
- Hulin Jin (ORCID: https://orcid.org/0000-0002-6896-2498)
- Zhiran Jin (ORCID: https://orcid.org/0000-0002-0946-5520)
Institutions
- Anhui University (CN)
- Sejong University (KR)
Publication Details
- Published
- 2025-03-21
- DOI
- https://doi.org/10.1109/iscait64916.2025.11010576
- Citations
- 1
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 1.43