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

1Citations
Citations of this article
10Readers
Mendeley users who have this article in their library.

Abstract

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.

Cite

CITATION STYLE

APA

Girdler, B., Caldbeck, W., & Bae, J. (2022, August 26). Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review. Frontiers in Systems Neuroscience. Frontiers Media S.A. https://doi.org/10.3389/fnsys.2022.836778

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free