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

Matthew S. Willsey, Samuel R. Nason-Tomaszewski, Scott R. Ensel, Hisham Temmar, Matthew J. Mender, Joseph T. Costello, Parag G. Patil and Cynthia A. Chestek ()
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Matthew S. Willsey: University of Michigan
Samuel R. Nason-Tomaszewski: University of Michigan
Scott R. Ensel: University of Michigan
Hisham Temmar: University of Michigan
Matthew J. Mender: University of Michigan
Joseph T. Costello: University of Michigan
Parag G. Patil: University of Michigan
Cynthia A. Chestek: University of Michigan

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: Abstract 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.

Date: 2022
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DOI: 10.1038/s41467-022-34452-w

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