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

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7 Author(s)
Justin C. Sanchez ; Department of Biomedical Engineering and the Miami Project to Cure Paralysis, University of Miami, Coral Gables, FL 33146 USA ; Aditya Tarigoppula ; John S. Choi ; Brandi T. Marsh
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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 naïve 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.

Published in:

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

Date of Conference:

April 27 2011-May 1 2011