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Rapid control and feedback rates enhance neuroprosthetic control

Author

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  • Maryam M. Shanechi

    (Viterbi School of Engineering, University of Southern California
    University of California, Berkeley)

  • Amy L. Orsborn

    (UC Berkeley — UCSF Joint Graduate Program in Bioengineering, University of California, Berkeley)

  • Helene G. Moorman

    (Helen Wills Neuroscience Institute, University of California, Berkeley)

  • Suraj Gowda

    (University of California, Berkeley)

  • Siddharth Dangi

    (University of California, Berkeley)

  • Jose M. Carmena

    (University of California, Berkeley
    UC Berkeley — UCSF Joint Graduate Program in Bioengineering, University of California, Berkeley
    Helen Wills Neuroscience Institute, University of California, Berkeley)

Abstract

Brain-machine interfaces (BMI) create novel sensorimotor pathways for action. Much as the sensorimotor apparatus shapes natural motor control, the BMI pathway characteristics may also influence neuroprosthetic control. Here, we explore the influence of control and feedback rates, where control rate indicates how often motor commands are sent from the brain to the prosthetic, and feedback rate indicates how often visual feedback of the prosthetic is provided to the subject. We developed a new BMI that allows arbitrarily fast control and feedback rates, and used it to dissociate the effects of each rate in two monkeys. Increasing the control rate significantly improved control even when feedback rate was unchanged. Increasing the feedback rate further facilitated control. We also show that our high-rate BMI significantly outperformed state-of-the-art methods due to higher control and feedback rates, combined with a different point process mathematical encoding model. Our BMI paradigm can dissect the contribution of different elements in the sensorimotor pathway, providing a unique tool for studying neuroprosthetic control mechanisms.

Suggested Citation

  • Maryam M. Shanechi & Amy L. Orsborn & Helene G. Moorman & Suraj Gowda & Siddharth Dangi & Jose M. Carmena, 2017. "Rapid control and feedback rates enhance neuroprosthetic control," Nature Communications, Nature, vol. 8(1), pages 1-10, April.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms13825
    DOI: 10.1038/ncomms13825
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    Cited by:

    1. Han-Lin Hsieh & Maryam M Shanechi, 2018. "Optimizing the learning rate for adaptive estimation of neural encoding models," PLOS Computational Biology, Public Library of Science, vol. 14(5), pages 1-34, May.
    2. Burkhart, Michael C., 2019. "A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding," Thesis Commons 4j3fu, Center for Open Science.

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