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Accès ouvert déclaré 2023 article

From monkeys to humans: observation-based EMG brain–computer interface decoders for humans with paralysis

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3Institutions déclarées
1Pays d’affiliation déclarés

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Le résumé fourni par la source

Abstract Objective . Intracortical brain–computer interfaces (iBCIs) aim to enable individuals with paralysis to control the movement of virtual limbs and robotic arms. Because patients’ paralysis prevents training a direct neural activity to limb movement decoder, most iBCIs rely on ‘observation-based’ decoding in which the patient watches a moving cursor while mentally envisioning making the movement. However, this reliance on observed target motion for decoder development precludes its application to the prediction of unobservable motor output like muscle activity. Here, we ask whether recordings of muscle activity from a surrogate individual performing the same movement as the iBCI patient can be used as target for an iBCI decoder. Approach . We test two possible approaches, each using data from a human iBCI user and a monkey, both performing similar motor actions. In one approach, we trained a decoder to predict the electromyographic (EMG) activity of a monkey from neural signals recorded from a human. We then contrast this to a second approach, based on the hypothesis that the low-dimensional ‘latent’ neural representations of motor behavior, known to be preserved across time for a given behavior, might also be preserved across individuals. We ‘transferred’ an EMG decoder trained solely on monkey data to the human iBCI user after using Canonical Correlation Analysis to align the human latent signals to those of the monkey. Main results . We found that both direct and transfer decoding approaches allowed accurate EMG predictions between two monkeys and from a monkey to a human. Significance . Our findings suggest that these latent representations of behavior are consistent across animals and even primate species. These methods are an important initial step in the development of iBCI decoders that generate EMG predictions that could serve as signals for a biomimetic decoder controlling motion and impedance of a prosthetic arm, or even muscle force directly through functional electrical stimulation.

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Le contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
From monkeys to humans: observation-based EMG brain–computer interface decoders for humans with paralysis
Date Crossref
01/10/2023
Éditeur
IOP Publishing
Type
journal-article

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.

Où se fait cette recherche

  • Northwestern University Department of Neuroscience pays non établi dans la notice
    Université ou école supérieure
  • University of Pittsburgh Department of Physical Medicine and Rehabilitation pays non établi dans la notice
    Université ou école supérieure
  • Shirley Ryan AbilityLab pays non établi dans la notice
    Établissement de santé

Department of Neuroscience — Northwestern University, Department of Physical Medicine and Rehabilitation — University of Pittsburgh et Shirley Ryan AbilityLab.

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Les sujets associés

EEG and Brain-Computer InterfacesMuscle activation and electromyography studiesGaze Tracking and Assistive Technology

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