Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review

Benton Girdler, William Caldbeck, Jihye Bae

Research output: Contribution to journalReview articlepeer-review


Creating flexible and robust brain machine interfaces (BMIs) is currently a popular topic of research that has been explored for decades in medicine, engineering, commercial, and machine-learning communities. In particular, the use of techniques using reinforcement learning (RL) has demonstrated impressive results but is under-represented in the BMI community. To shine more light on this promising relationship, this article aims to provide an exhaustive review of RL’s applications to BMIs. Our primary focus in this review is to provide a technical summary of various algorithms used in RL-based BMIs to decode neural intention, without emphasizing preprocessing techniques on the neural signals and reward modeling for RL. We first organize the literature based on the type of RL methods used for neural decoding, and then each algorithm’s learning strategy is explained along with its application in BMIs. A comparative analysis highlighting the similarities and uniqueness among neural decoders is provided. Finally, we end this review with a discussion about the current stage of RLBMIs including their limitations and promising directions for future research.

Original languageEnglish
Article number836778
JournalFrontiers in Systems Neuroscience
StatePublished - Aug 26 2022

Bibliographical note

Publisher Copyright:
Copyright © 2022 Girdler, Caldbeck and Bae.


  • brain machine interface (BMI)
  • neural decoder
  • neural interface
  • policy optimization
  • reinforcement learning (RL)
  • value function approximation

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Developmental Neuroscience
  • Cognitive Neuroscience
  • Cellular and Molecular Neuroscience


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