Real-time Decomposition of Multi-Channel Intramuscular EMG Signals Recorded by Micro-Electrode Arrays in Humans
Artikel i vetenskaplig tidskrift, 2025

Intramuscular electromyography (iEMG) decomposition identifies motor neuron (MN) discharge timings from interference iEMG recordings. When this is performed in real-time, the extracted neural information can be used for establishing human-machine interfaces. We propose a multi-channel real-time decomposition algorithm based on a Hidden Markov Model of EMG and a Bayesian filter to estimate the spike trains of motor units (MUs) and their action potentials (MUAPs). The multi-channel framework of Bayesian modelling and filtering was implemented into parallel computation using multiple GPU clusters, which ensures computational speed compatible with real-time decomposition. A decomposed-checked channel strategy is then proposed for arranging channels into groups to be processed in related GPU clusters. The algorithm was validated on six 16-channel simulated signals, three 32-channel experimental signals acquired from the human tibialis anterior muscle, and two 16-channel experimental signals acquired from the abductor digiti minimi muscle with thin-film implanted electrodes. All signals were decomposed in real time with an average decomposition accuracy >90%. In conclusion, the proposed multi-channel iEMG decomposition algorithm can be applied to implanted multi-channel electrode arrays to establish human-machine interfaces with high-information transfer.

Bayes methods

real-time decomposition

recursive estimation

Hidden Markov models

multi-channel iEMG decomposition

Författare

Tianyi Yu

Imperial College London

Silvia Muceli

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Imperial College London

Konstantin Akhmadeev

École Centrale de Nantes

Eric Le Carpentier

École Centrale de Nantes

Yannick Aoustin

Laboratoire des Sciences du Numérique de Nantes

Dario Farina

Imperial College London

IEEE Transactions on Biomedical Engineering

0018-9294 (ISSN) 15582531 (eISSN)

Vol. In Press

Ämneskategorier (SSIF 2025)

Signalbehandling

DOI

10.1109/TBME.2025.3556853

Mer information

Senast uppdaterat

2025-04-23