Evidence map›Paper›PMID 39356736›Full record

ArticleeLife2024

I-Spin live, an open-source software based on blind-source separation for real-time decoding of motor unit activity in humans.

Julien Rossato, François Hug, Kylie Tucker, Ciara Gibbs, Lilian Lacourpaille, Dario Farina, Simon Avrillon

Abstract read
In one paragraph

Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. The Volitional Control of Individual Motor Units Is Constrained within Low-Dimensional Neural Manifolds by Common Inputs.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024
    Article
  6. 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

7 authors.

Julien RossatoNantes Université, Laboratory "Movement, Interactions, Performance" (UR 4334), Nantes, France.
François HugUniversité Côte d'Azur, LAMHESS, Nice, France.
Kylie TuckerThe University of Queensland, School of Biomedical Sciences, Brisbane, Australia.
Ciara GibbsDepartment of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom.
Lilian LacourpailleNantes Université, Laboratory "Movement, Interactions, Performance" (UR 4334), Nantes, France.
Dario FarinaDepartment of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-7883-2697
Simon AvrillonDepartment of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-2226-3528

Funding

Agence Nationale de la Recherche ANR-15-IDEX-01Biotechnology and Biological Sciences Research Council BB/V00896XEngineering and Physical Sciences Research Council EP/T020970European Research Council #810346
6 · The paper itself

Abstract

Decoding the activity of individual neural cells during natural behaviours allows neuroscientists to study how the nervous system generates and controls movements. Contrary to other neural cells, the activity of spinal motor neurons can be determined non-invasively (or minimally invasively) from the decomposition of electromyographic (EMG) signals into motor unit firing activities. For some interfacing and neuro-feedback investigations, EMG decomposition needs to be performed in real time. Here, we introduce an open-source software that performs real-time decoding of motor neurons using a blind-source separation approach for multichannel EMG signal processing. Separation vectors (motor unit filters) are optimised for each motor unit from baseline contractions and then re-applied in real time during test contractions. In this way, the firing activity of multiple motor neurons can be provided through different forms of visual feedback. We provide a complete framework with guidelines and examples of recordings to guide researchers who aim to study movement control at the motor neuron level. We first validated the software with synthetic EMG signals generated during a range of isometric contraction patterns. We then tested the software on data collected using either surface or intramuscular electrode arrays from five lower limb muscles (gastrocnemius lateralis and medialis, vastus lateralis and medialis, and tibialis anterior). We assessed how the muscle or variation of contraction intensity between the baseline contraction and the test contraction impacted the accuracy of the real-time decomposition. This open-source software provides a set of tools for neuroscientists to design experimental paradigms where participants can receive real-time feedback on the output of the spinal cord circuits.

Indexed as

ElectromyographyMotor NeuronsSoftwareAdultFemaleHumansMaleMuscle, SkeletalSignal Processing, Computer-AssistedYoung Adultdecompositionelectromyographyhumanmotor unitneural decodingneuroscience

Identifiers

PMID39356736
PMCPMC11446545

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Registered trials

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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.