Reinforcement learning based fast self-recalibrating decoder for intracortical brain–machine interface

Peng Zhang, Lianying Chao, Yuting Chen, Xuan Ma, Weihua Wang, Jiping He, Jian Huang, Qiang Li*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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 decoder. However, quickly exploring new knowledge while maintaining a good performance remains a challenge in RL-based decoders. Methods: To solve this problem, we proposed an attention-gated RL-based algorithm combining transfer learning, mini-batch, and weight updating schemes to accelerate the weight updating and avoid over-fitting. The proposed algorithm was tested on intracortical neural data recorded from two monkeys to decode their reaching positions and grasping gestures. Results: The decoding results showed that our proposed algorithm achieved an approximate 20% increase in classification accuracy compared to that obtained by the non-retrained classifier and even achieved better classification accuracy than the daily retraining classifier. Moreover, compared with a conventional RL method, our algorithm improved the accuracy by approximately 10% and the online weight updating speed by approximately 70 times. Conclusions: This paper proposed a self-recalibrating decoder which achieved a good and robust decoding performance with fast weight updating and might facilitate its application in wearable device and clinical practice.

Original languageEnglish (US)
Article number5528
Pages (from-to)1-19
Number of pages19
JournalSensors (Switzerland)
Issue number19
StatePublished - Oct 1 2020


  • Adaptive decoder
  • Intracortical brain–machine interface
  • Reinforcement learning
  • Transfer learning

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering


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