CLTNet: A Hybrid Deep Learning Model for Motor Imagery Classification
The innovation of the CLTNet model is that it integrates multiple network architectures, which offers a more comprehensive understanding of the characteristics of the EEG signals during motor imagery, providing a more comprehensive perspective and establishing a new benchmark for future research in this area.
Authors
- Jian Zhao (ORCID: https://orcid.org/0000-0003-3265-6461)
- 和憲 神谷 (ORCID: https://orcid.org/0009-0000-7512-409X)
- Mengyuan Zhang (ORCID: https://orcid.org/0009-0009-5279-3712)
- Yan Sun (ORCID: https://orcid.org/0000-0002-2017-2411)
- Xiao Ma (ORCID: https://orcid.org/0009-0007-8697-3071)
- Ting-Wei Chen (ORCID: https://orcid.org/0009-0003-0590-4789)
Institutions
- Changchun University of Science and Technology (CN)
- Ministry of Education of the People's Republic of China (CN)
- Health Commission of Jilin Province (CN)
Publication Details
- Journal
- Brain Sciences
- Published
- 2025-01-27
- DOI
- https://doi.org/10.3390/brainsci15020124
- Citations
- 12
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 17.17