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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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| Publicado no: | Sensors (Basel) |
|---|---|
| Main Authors: | , , , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
MDPI
2020
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7582276/ https://ncbi.nlm.nih.gov/pubmed/32992539 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/s20195528 |
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