Symbiotic Brain-Machine Interface decoding using simultaneous motor and reward neural representation

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

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

1 Scopus citations

Abstract

In this work, we design and test a framework for neural decoding in Brain-Machine Interfaces based on the Perception Action Reward Cycle (PARC). Here the neural decoder in the BMI learns to translate motor neural states in the primary motor cortex (M1) into actions based on a reward signal estimated directly from Neucleus Accumbens (NAcc). The control architecture was designed based on the Actor-Critic method of Reinforcement Learning. We tested the decoding performance by simultaneous recording the M1 and NAcc neural data in a rat during a robot-assisted reaching task. This work shows that a BMI can be trained from a nave state to perform a reaching task using motor and error feedback signals directly from the brain.

Original languageEnglish (US)
Title of host publication2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011
Pages597-600
Number of pages4
DOIs
StatePublished - Jul 20 2011
Event2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011 - Cancun, Mexico
Duration: Apr 27 2011May 1 2011

Publication series

Name2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011

Other

Other2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011
CountryMexico
CityCancun
Period4/27/115/1/11

ASJC Scopus subject areas

  • Neuroscience(all)

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