Control of a center-out reaching task using a reinforcement learning Brain-Machine Interface

Justin C. Sanchez, Aditya Tarigoppula, John S. Choi, Brandi T. Marsh, Pratik Y. Chhatbar, Babak Mahmoudi, Joseph T. Francis

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

17 Scopus citations

Abstract

In this work, we develop an experimental primate test bed for a center-out reaching task to test the performance of reinforcement learning based decoders for Brain-Machine Interfaces. Neural recordings obtained from the primary motor cortex were used to adapt a decoder using only sequences of neuronal activation and reinforced interaction with the environment. From a nave state, the system was able to achieve 100% of the targets without any a priori knowledge of the correct neural-to-motor mapping. Results show that the coupling of motor and reward information in an adaptive BMI decoder has the potential to create more realistic and functional models necessary for future BMI control.

Original languageEnglish (US)
Title of host publication2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011
Pages525-528
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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