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

Published

Conference Paper

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. © 2011 IEEE.

Full Text

Duke Authors

Cited Authors

  • Sanchez, JC; Tarigoppula, A; Choi, JS; Marsh, BT; Chhatbar, PY; Mahmoudi, B; Francis, JT

Published Date

  • July 20, 2011

Published In

  • 2011 5th International Ieee/Embs Conference on Neural Engineering, Ner 2011

Start / End Page

  • 525 - 528

International Standard Book Number 13 (ISBN-13)

  • 9781424441402

Digital Object Identifier (DOI)

  • 10.1109/NER.2011.5910601

Citation Source

  • Scopus