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Clustering Neural Patterns in Kernel Reinforcement Learning Assists Fast Brain Control in Brain-Machine Interfaces

  • Published:2019-09 Issue:9 Volume:27 Page:1684-1694
  • ISSN:1534-4320
  • Container-title:IEEE Transactions on Neural Systems and Rehabilitation Engineering
  • language:
  • Short-container-title:IEEE Trans. Neural Syst. Rehabil. Eng.

Author:

Zhang XiangORCID,Libedinsky Camilo,So RosaORCID,Principe Jose C.ORCID,Wang YiwenORCID

Funder

Innovation and Technology Commission

National Natural Science Foundation of China

Hong Kong University of Science and Technology

HKUST startup Fund

A*Star JCO Grant

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation

Cited by 18 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Novel KL Divergence Optimization Method for Aligning Neural Population Patterns During Task Learning;2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC);2023-10-01

2. A Kernel Reinforcement Learning Decoding Framework Integrating Neural and Feedback Signals for Brain Control*;2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC);2023-07-24

3. Kernel Reinforcement Learning-Assisted Adaptive Decoder Facilitates Stable and Continuous Brain Control Tasks;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2023

4. Quantitative Modeling on Nonstationary Neural Spikes: From Reinforcement Learning to Point Process;Handbook of Neuroengineering;2023

5. Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review;Frontiers in Systems Neuroscience;2022-08-26
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