Motor unit sampling from intramuscular micro-electrode array recordings
File(s)
Author(s)
Grison, Agnese
Pereda, Jaime Ibanez
Farina, Dario
Type
Journal Article
Abstract
Recordings of electrical activity from muscles allow us to identify the activity of pools of spinal motor neurons that send the neural drive for muscle activation. Decoding motor unit and motor neuron activity from muscle recordings can be performed by high-density (HD) electrode systems, both non-invasively (surface, HD-sEMG) and invasively (intramuscular, HD-iEMG). HD-sEMG recordings are obtained by grids placed on the skin surface while HD-iEMG signals can be acquired by micro-electrode arrays. While it has been shown that HD-iEMG allows the accurate decoding of a larger number of motor units when compared to HD-sEMG, the dependence of motor unit yield on the parameters of the micro-electrode arrays is still unexplored. Here, we used recently developed HD-iEMG electrodes to record from hundreds of recording sites within the muscle. This allowed us to investigate the impact of electrode number, inter-electrode distance, and the number of muscle insertions on the ability to sample motor units within the muscle. Specifically, we recorded both HD-sEMG and HD-iEMG from the Tibialis Anterior muscle of two healthy subjects at various contraction intensities (10%, 30%, and 70% of maximum voluntary contraction, MVC). For the first time, we present intramuscular recordings with more than 140 electrodes inside a single muscle, achieved through multiple implants of high-density micro-electrode arrays. Through systematic offline analyses of these recordings, we tested different electrode configurations to identify optimal setups for accurately capturing motor unit activity. The results revealed that the density of electrodes in the micro-electrode arrays is the most critical factor for maximising the number of identified motor units and ensuring very high accuracy. Comparisons between intramuscular and surface recordings also confirmed that HD-iEMG consistently captures larger and more stable numbers of motor units across subjects and contraction levels. These results underscore the potential of HD-iEMG as a powerful tool for both clinical and research settings, particularly when precise motor unit decomposition is crucial.
Date Issued
2025-01-01
Date Acceptance
2025-01-13
Citation
IEEE transactions on neural systems and rehabilitation engineering, 2025, 33, pp.620-629
ISSN
1534-4320
Publisher
IEEE
Start Page
620
End Page
629
Journal / Book Title
IEEE transactions on neural systems and rehabilitation engineering
Volume
33
Copyright Statement
© 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
License URL
Subjects
Accuracy
Ankle
Convolution
DECOMPOSITION
Electrodes
Electromyography
EMG
Engineering
Engineering, Biomedical
high-density
intramuscular
Life Sciences & Biomedicine
motor units
Motors
Muscles
MUSCLES
Neurons
PHYSIOLOGY
Recording
Rehabilitation
Science & Technology
Signal resolution
SURFACE EMG
Technology
Publication Status
Published
Date Publish Online
2025-01-17