Reinforcement Learning Based Fast Self-Recalibrating Decoder for Intracortical Brain–Machine Interface

Background: For the nonstationarity of neural recordings in intracortical brain–machine interfaces, daily retraining in a supervised manner is always required to maintain the performance of the decoder. This problem can be improved by using a reinforcement learning (RL) based self-recalibrating deco...

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Detalles Bibliográficos
Autores principales: Zhang, Peng, Chao, Lianying, Chen, Yuting, Ma, Xuan, Wang, Weihua, He, Jiping, Huang, Jian, Li, Qiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7582276/
https://www.ncbi.nlm.nih.gov/pubmed/32992539
http://dx.doi.org/10.3390/s20195528

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7582276/
https://www.ncbi.nlm.nih.gov/pubmed/32992539
http://dx.doi.org/10.3390/s20195528

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