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From monkeys to humans: observation-basedEMGbrain-computer interface decoders for humans with paralysis.

Fabio Rizzoglio1, Ege Altan1,2, Xuan Ma1

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Researchers developed new methods for brain-computer interfaces (BCIs) to predict muscle activity by using data from another individual. This advance could improve prosthetic control for people with paralysis.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Intracortical brain-computer interfaces (iBCIs) typically use observed movements for decoder training, limiting their use for predicting unobservable outputs like muscle activity.
  • Existing iBCI methods cannot directly decode intended muscle activity due to paralysis preventing direct training.
  • There is a need for novel iBCI decoding strategies to enable control of advanced prosthetics and functional electrical stimulation.

Purpose of the Study:

  • To investigate the feasibility of using muscle activity recordings from a surrogate individual to train iBCI decoders.
  • To determine if neural representations of motor behavior are conserved across individuals and species for decoder transfer.
  • To develop iBCI decoding methods capable of predicting electromyographic (EMG) signals.

Main Methods:

  • Two decoding approaches were tested using neural data from a human iBCI user and a monkey performing similar tasks.
  • A direct decoding approach trained a model to predict monkey EMG from human neural signals.
  • A transfer decoding approach aligned latent neural signals between species using Canonical Correlation Analysis to enable decoder transfer.

Main Results:

  • Both direct and transfer decoding methods successfully predicted EMG activity with high accuracy.
  • Accurate EMG predictions were achieved between two monkeys and from a monkey to a human.
  • The study demonstrated the effectiveness of cross-species and cross-individual decoder transfer.

Conclusions:

  • Latent neural representations of motor behavior are consistent across individuals and even across primate species.
  • These findings represent a significant step towards developing iBCI decoders that predict EMG signals for prosthetic control.
  • The developed methods could enable biomimetic control of prosthetic arms and functional electrical stimulation for restoring movement and impedance.