Evidence map›Paper›PMID 34788156›Full record

Trial reportJournal of neurophysiology2021

Sensing and decoding the neural drive to paralyzed muscles during attempted movements of a person with tetraplegia using a sleeve array.

Jordyn E Ting, Alessandro Del Vecchio, Devapratim Sarma, Nikhil Verma, Samuel C Colachis, Nicholas V Annetta, Jennifer L Collinger, Dario Farina, Douglas J Weber

Open access · greenAbstract readClinical Trial
In one paragraph

Trial report in Journal of neurophysiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
3.4field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 59 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. High performance sorting of motor unit action potentials with EMUsort.bioRxiv : the preprint server for biology · 2026
    Article
  5. Preliminary Investigation of Myoelectric Control of an Assistive Neck Exoskeleton by Individuals With Amyotrophic Lateral Sclerosis.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026
    Article
  6. Article
  7. Predicting Motor Intent from Residual Neck Muscle Activity in Individuals with Neck Weakness from ALS.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025
    Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. The Forces Generated by Agonist Muscles during Isometric Contractions Arise from Motor Unit Synergies.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023
    Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 3 countries.

Jordyn E TingRehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, Pennsylvania.ORCID 0000-0002-3453-7450
Alessandro Del VecchioDepartment of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.ORCID 0000-0002-7118-5554
Devapratim SarmaRehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, Pennsylvania.
Nikhil VermaDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania.
Samuel C ColachisMedical Devices and Neuromodulation Group, Battelle Memorial Institute, Columbus, Ohio.ORCID 0000-0002-7131-8206
Nicholas V AnnettaMedical Devices and Neuromodulation Group, Battelle Memorial Institute, Columbus, Ohio.ORCID 0000-0002-4236-6356
Jennifer L CollingerRehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, Pennsylvania.ORCID 0000-0002-4517-5395
Dario FarinaDepartment of Bioengineering, Imperial College London, London, United Kingdom.ORCID 0000-0002-7883-2697
Douglas J WeberDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania.ORCID 0000-0002-9782-3497
University of Pittsburgh · USBattelle · USCarnegie Mellon University · USFriedrich-Alexander-Universität Erlangen-Nürnberg · DEImperial College London · GB

Funding

A Biomimetic Approach Towards a Dexterous NeuroprosthesisUH3NS107714 · NINDS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BONINGER, MICHAEL L. · 2018 to 2023
$7.1M
Context-dependent processing in sensorimotor cortexU01NS108922 · NINDS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI COLLINGER, JENNIFER L. · 2018 to 2019
$1.2M
NINDS NIH HHS UH3 NS107714
6 · The paper itself

Abstract

Motor neurons convey information about motor intent that can be extracted and interpreted to control assistive devices. However, most methods for measuring the firing activity of single neurons rely on implanted microelectrodes. Although intracortical brain-computer interfaces (BCIs) have been shown to be safe and effective, the requirement for surgery poses a barrier to widespread use that can be mitigated by instead using noninvasive interfaces. The objective of this study was to evaluate the feasibility of deriving motor control signals from a wearable sensor that can detect residual motor unit activity in paralyzed muscles after chronic cervical spinal cord injury (SCI). Despite generating no observable hand movement, volitional recruitment of motor units below the level of injury was observed across attempted movements of individual fingers and overt wrist and elbow movements. Subgroups of motor units were coactive during flexion or extension phases of the task. Single digit movement intentions were classified offline from the electromyogram (EMG) power [root-mean-square (RMS)] or motor unit firing rates with median classification accuracies >75% in both cases. Simulated online control of a virtual hand was performed with a binary classifier to test feasibility of real-time extraction and decoding of motor units. The online decomposition algorithm extracted motor units in 1.2 ms, and the firing rates predicted the correct digit motion 88 ± 24% of the time. This study provides the first demonstration of a wearable interface for recording and decoding firing rates of motor units below the level of injury in a person with motor complete SCI.

Indexed as

QuadriplegiaSpinal Cord InjuriesWearable Electronic DevicesAdultElectromyographyFeasibility StudiesHandHumansMachine LearningMaleMuscle, SkeletalNeurological RehabilitationRecruitment, NeurophysiologicaldecodingEMGmotor unitneuroprostheticsspinal cord injury

Identifiers

PMID34788156
PMCPMC8715052
OpenAlexW3213796871

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.