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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...
| Autores principales: | , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2020
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| Materias: | |
| 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 |