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

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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Bibliographic Details
Main Authors: Peng Zhang, Lianying Chao, Yuting Chen, Xuan Ma, Weihua Wang, Jiping He, Jian Huang, Qiang Li
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Sensors
Subjects:
intracortical brain–machine interface
reinforcement learning
adaptive decoder
transfer learning
Online Access:https://www.mdpi.com/1424-8220/20/19/5528

Internet

https://www.mdpi.com/1424-8220/20/19/5528

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