Neuronal tuning in a brain-machine interface during Reinforcement Learning

Babak Mahmoudi, Jack Digiovanna, Jose C. Principe, Justin C. Sanchez

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

2 Scopus citations

Abstract

In this research, we have used neural tuning to quantify the neural representation of prosthetic arm's actions in a new framework of BMI, which is based on Reinforcement Learning (RLBMI). We observed that through closed-loop brain control, the neural representation has changed to encode robot actions that maximize rewards. This is an interesting result because in our paradigm robot actions are directly controlled by a Computer Agent (CA) with reward states compatible with the user's rewards. Through co-adaptation, neural modulation is used to establish the value of robot actions to achieve reward.

Original languageEnglish
Title of host publicationProceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"
Pages4491-4494
Number of pages4
StatePublished - Dec 1 2008
Externally publishedYes
Event30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - Vancouver, BC, Canada
Duration: Aug 20 2008Aug 25 2008

Other

Other30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
CountryCanada
CityVancouver, BC
Period8/20/088/25/08

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

Mahmoudi, B., Digiovanna, J., Principe, J. C., & Sanchez, J. C. (2008). Neuronal tuning in a brain-machine interface during Reinforcement Learning. In Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology" (pp. 4491-4494). [4650210]