I-Spin live, an open-source software based on blind-source separation for real-time decoding of motor unit activity in humans
File(s)
Published version
Author(s)
Type
Journal Article
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.
Date Issued
2024-10-02
Date Acceptance
2024-10-01
Citation
eLife, 2024, 12
ISSN
2050-084X
Publisher
eLife Sciences Publications Ltd
Journal / Book Title
eLife
Volume
12
Copyright Statement
Copyright Rossato et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/39356736
PII: 88670
Subjects
ACTION-POTENTIALS
Biology
decomposition
electromyography
IDENTIFICATION
Life Sciences & Biomedicine
Life Sciences & Biomedicine - Other Topics
motor unit
neural decoding
PHYSIOLOGY
Science & Technology
Publication Status
Published
Coverage Spatial
England
Article Number
RP88670
Date Publish Online
2024-10-02