Brain-machine interface control via reinforcement learning

Jack DiGiovanna, Babak Mahmoudi, Jeremiah Mitzelfelt, Justin C. Sanchez, Jose C. Principe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

We investigate the capabilities of reinforcement learning (RL) to create a brain-machine interface (BMI) that uses Q(λ) learning to find the functional mapping between neural activity and intended behavior. This paradigm shift is intended to address the issue of paralyzed and amputee patients whom are physically unable to move, which is necessary to train traditional supervised learning BMIs. We created a RLBMI architecture incorporating a rat behavioral paradigm for prosthetic arm control. The performance results show 'proof of concept' that RLBMI can learn the temporal structure of neural signals to control a prosthetic arm.

Original languageEnglish (US)
Title of host publicationProceedings of the 3rd International IEEE EMBS Conference on Neural Engineering
Pages530-533
Number of pages4
DOIs
StatePublished - Sep 25 2007
Event3rd International IEEE EMBS Conference on Neural Engineering - Kohala Coast, HI, United States
Duration: May 2 2007May 5 2007

Publication series

NameProceedings of the 3rd International IEEE EMBS Conference on Neural Engineering

Other

Other3rd International IEEE EMBS Conference on Neural Engineering
CountryUnited States
CityKohala Coast, HI
Period5/2/075/5/07

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ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Neuroscience (miscellaneous)

Cite this

DiGiovanna, J., Mahmoudi, B., Mitzelfelt, J., Sanchez, J. C., & Principe, J. C. (2007). Brain-machine interface control via reinforcement learning. In Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering (pp. 530-533). [4227331] (Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering). https://doi.org/10.1109/CNE.2007.369726