A novel motor imagery brain-computer interface classification framework based on augmented covariance matrix: deep Riemannian geometry learning with self-attention mechanism
- DOI
- 10.1007/s11571-026-10548-7
- Published
- 2026-09-21
- Container
- Cognitive Neurodynamics
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/s11571-026-10548-7,
title = {A novel motor imagery brain-computer interface classification framework based on augmented covariance matrix: deep Riemannian geometry learning with self-attention mechanism},
author = {Tian Yan and Aimin Zhang and Zengyao Yang and Hechong Su and Yidan Ma and Yue Wu and Li Yan and Yichen Wang and Fei Guo and Jianfu Cao},
year = {2026},
journal = {Cognitive Neurodynamics},
doi = {10.1007/s11571-026-10548-7},
url = {https://doi.org/10.1007/s11571-026-10548-7}
}RIS
TY - JOUR TI - A novel motor imagery brain-computer interface classification framework based on augmented covariance matrix: deep Riemannian geometry learning with self-attention mechanism AU - Tian Yan AU - Aimin Zhang AU - Zengyao Yang AU - Hechong Su AU - Yidan Ma AU - Yue Wu AU - Li Yan AU - Yichen Wang AU - Fei Guo AU - Jianfu Cao PY - 2026 JO - Cognitive Neurodynamics DO - 10.1007/s11571-026-10548-7 UR - https://doi.org/10.1007/s11571-026-10548-7 ER -
APA
Yan, T., Zhang, A., Yang, Z., Su, H., Ma, Y., Wu, Y., Yan, L., Wang, Y., Guo, F., & Cao, J. (2026). A novel motor imagery brain-computer interface classification framework based on augmented covariance matrix: deep Riemannian geometry learning with self-attention mechanism. Cognitive Neurodynamics. https://doi.org/10.1007/s11571-026-10548-7
Source records
- crossref · retrieved 2026-09-25T05:00:29.603Z