4.8 Article

Real-time brain-machine interface in non-human primates achieves high-velocity prosthetic finger movements using a shallow feedforward neural network decoder

Journal

NATURE COMMUNICATIONS
Volume 13, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41467-022-34452-w

Keywords

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Funding

  1. NSF [1926576]
  2. NIH [R01GM111293, F31HD098804]
  3. NSF GRFP
  4. A. Alfred Taubman Medical Research Institute
  5. Craig H. Neilsen Foundation [315108]
  6. MCubed project [1482]
  7. Division Of Behavioral and Cognitive Sci
  8. Direct For Social, Behav & Economic Scie [1926576] Funding Source: National Science Foundation

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Despite the rapid progress and interest in brain-machine interfaces that restore motor function, the study demonstrates that shallow-layer neural network decoders outperform and enable higher velocity finger movements than the current linear decoding standard.
Despite the rapid progress and interest in brain-machine interfaces that restore motor function, the performance of prosthetic fingers and limbs has yet to mimic native function. The algorithm that converts brain signals to a control signal for the prosthetic device is one of the limitations in achieving rapid and realistic finger movements. To achieve more realistic finger movements, we developed a shallow feed-forward neural network to decode real-time two-degree-of-freedom finger movements in two adult male rhesus macaques. Using a two-step training method, a recalibrated feedback intention-trained (ReFIT) neural network is introduced to further improve performance. In 7 days of testing across two animals, neural network decoders, with higher-velocity and more natural appearing finger movements, achieved a 36% increase in throughput over the ReFIT Kalman filter, which represents the current standard. The neural network decoders introduced herein demonstrate real-time decoding of continuous movements at a level superior to the current state-of-the-art and could provide a starting point to using neural networks for the development of more naturalistic brain-controlled prostheses. Despite the rapid progress and interest in brain-machine interfaces that restore motor function, the performance of prosthetic fingers and limbs has yet to mimic native function. Here, the authors demonstrate that shallow-layer neural network decoders outperform and enable higher velocity finger movements than the current linear decoding standard.

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