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Editorial | Open Access

Brain-computer interface (BCI) in clinical neurorestorative practices

Yunfa Fua, Yuhang Xuea, Xiaogang Chenb, Yong Huc,d( )
Faculty of Information Engineering and Automation, Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, China
Department of Orthopaedics & Traumatology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
Orthopedics Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518057, Guangdong, China
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References

1

Sun YK, Chen XG, Gao XR. The potential of diverse brainecomputer interface signal acquisition techniques in neurorestoratology. J Neurorestoratol. 2024;12(3):100138. https://doi.org/10.1016/j.jnrt.2024.100138.
Crossref Google Scholar
2

Ma ZZ, Wu JJ, Cao Z, et al. Motor imagery-based brain-computer interface rehabilitation programs enhance upper extremity performance and cortical activation in stroke patients. J NeuroEng Rehabil. 2024;21(1):91. https://doi.org/10.1186/s12984-024-01387-w.
Crossref Google Scholar
3

Metzger SL, Littlejohn KT, Silva AB, et al. A high-performance neuroprosthesis for speech decoding and avatar control. Nature. 2023;620(7976):1037-1046. https://doi.org/10.1038/s41586-023-06443-4.
Crossref Google Scholar
4

Levett JJ, Elkaim LM, Niazi F, et al. Invasive brain computer interface for motor restoration in spinal cord injury: a systematic review. Neuromodulation. 2024;27(4):597-603. https://doi.org/10.1016/j.neurom.2023.10.006.
Crossref Google Scholar
5

Lorach H, Galvez A, Spagnolo V, et al. Walking naturally after spinal cord injury using a brain-spine interface. Nature. 2023;618(7963):126-133. https://doi.org/10.1038/s41586-023-06094-5.
Crossref Google Scholar
6

Chen YX, Wang F, Li TW, et al. Considerations and discussions on the clear definition and definite scope of brain-computer interfaces. Front Neurosci. 2024;18:1449208. https://doi.org/10.3389/fnins.2024.1449208.
Crossref Google Scholar
7

Nan WY, Yang WJ, Gong AM, et al. Successful learning of alpha up-regulation through neurofeedback training modulates sustained attention. Neuropsychologia. 2024;195:108804. https://doi.org/10.1016/j.neuropsychologia.2024.108804.
Crossref Google Scholar
8

Fu YF, Chen XG, Hu Y. Correct understanding of brainecomputer interfaces. J Neurorestoratol. 2024;12(3):100139. https://doi.org/10.1016/j.jnrt.2024.100139.
Crossref Google Scholar
Journal of Neurorestoratology
Volume 13 Issue 2,
April 2025
DOI: 10.1016/j.jnrt.2025.100188
Cite this article:
Fu Y, Xue Y, Chen X, et al. Brain-computer interface (BCI) in clinical neurorestorative practices. Journal of Neurorestoratology, 2025, 13(2). https://doi.org/10.1016/j.jnrt.2025.100188

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Received: 06 January 2025
Published: 01 April 2025
© 2025 The Author(s).

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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