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; Scott R. Ensel; Hisham Temmar; Matthew J. Mender; Joseph T. Costello; Parag G. Patil; Cynthia A. Chestek · 2022 · Nature Communications
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
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
Abstract by Matthew S. Willsey; Samuel R. Nason; Scott R. Ensel; Hisham Temmar; Matthew J. Mender; Joseph T. Costello; Parag G. Patil; Cynthia A. Chestek, Nature Communications (2022) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1038/s41467-022-34452-w