Deep Learning for Scalp-Level Nonlinear Source Separation in Electroencephalogram: A Comparative Evaluation of Spatiotemporal Architectures
- DOI
- 10.1109/tnsre.2026.3724828
- Published
- 2026
- Container
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tnsre.2026.3724828,
title = {Deep Learning for Scalp-Level Nonlinear Source Separation in Electroencephalogram: A Comparative Evaluation of Spatiotemporal Architectures},
author = {Liyuan Ma and Xiaogang Hu},
year = {2026},
journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
doi = {10.1109/tnsre.2026.3724828},
url = {https://doi.org/10.1109/tnsre.2026.3724828}
}RIS
TY - JOUR TI - Deep Learning for Scalp-Level Nonlinear Source Separation in Electroencephalogram: A Comparative Evaluation of Spatiotemporal Architectures AU - Liyuan Ma AU - Xiaogang Hu PY - 2026 JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering DO - 10.1109/tnsre.2026.3724828 UR - https://doi.org/10.1109/tnsre.2026.3724828 ER -
APA
Ma, L., & Hu, X. (2026). Deep Learning for Scalp-Level Nonlinear Source Separation in Electroencephalogram: A Comparative Evaluation of Spatiotemporal Architectures. IEEE Transactions on Neural Systems and Rehabilitation Engineering. https://doi.org/10.1109/tnsre.2026.3724828
Source records
- crossref · retrieved 2026-09-26T06:06:26.404Z