Simulation of Motor Unit Action Potential Recordings From Intramuscular Multichannel Scanning Electrodes

Akhmadeev, Konstantin; Yu, Tianyi; Carpentier, Éric Le; Aoustin, Yannick; Farina, Dario

doi:10.1109/tbme.2019.2953680

Cited by 23 publications

(9 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A pool of 200 motor units was simulated to test whether increasing the density and the size of surface grids of electrodes would impact the number of identifiable motor units. The simulations were based on an anatomical model of a cylindrical muscle volume with parallel fibres (Farina et al, 2008; Konstantin et al, 2020), where subcutaneous and skin layers separate the muscle from the surface electrodes. Specifically, we set the radius of the muscle to 25.4 mm and the thicknesses of the subcutaneous and skin layers to 5 mm and 1 mm, respectively.…”

Section: Methodsmentioning

confidence: 99%

Larger and denser: an optimal design for surface grids of EMG electrodes to identify greater and more representative samples of motor units

Caillet

Avrillon

Kundu

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

The spinal motor neurons are the only neural cells whose individual activity can be non-invasively identified using grids of electromyographic (EMG) electrodes and source separation methods, i.e., EMG decomposition. In this study, we combined computational and experimental approaches to assess how the design parameters of grids of electrodes influence the number and characteristics of the motor units identified. We first computed the percentage of unique motor unit action potentials that could be theoretically discriminated in a pool of 200 simulated motor units when recorded with grids of various sizes and interelectrode distances (IED). We then identified motor units from experimental EMG signals recorded in six participants with grids of various sizes (range: 2-36 cm2) and IED (range: 4-16 mm). Increasing both the density and the number of electrodes, as well as the size of the grids, increased the number of motor units that the EMG decomposition could theoretically discriminate, i.e., up to 82.5% of the simulated pool (range: 30.5-82.5%). Experimentally, the configuration with the largest number of electrodes and the shortest IED maximized the number of motor units identified (56 +/- 14; range: 39-79) and the percentage of low-threshold motor units identified (29 +/- 14%). Finally, we showed with a prototyped grid of 400 electrodes (IED: 2 mm) that the number of identified motor units plateaus beyond an IED of 2-4 mm. These results showed that larger and denser surface grids of electrodes help to identify a larger and more representative pool of motor units than currently reported in experimental studies.

show abstract

Section: Methodsmentioning

confidence: 99%

Larger and denser: an optimal design for surface grids of EMG electrodes to identify greater and more representative samples of motor units

Caillet

Avrillon

Kundu

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…the spike trains of simulated MU. To this end, we have created a cylindrical volume containing three layers (muscle, fat, and skin) with a radius of 10 mm, 4 mm and 1 mm, respectively, and an average fiber length of 200 mm [28][29][30]. The muscle had 125 664 fibers parallel to the skin that were grouped into 150 MUs.…”

Section: Simulation Of Hd-emg Signalsmentioning

confidence: 99%

A convolutional neural network to identify motor units from high-density surface electromyography signals in real time

et al. 2021

View full text Add to dashboard Cite

Objectives. This paper aims to investigate the feasibility and the validity of applying deep convolutional neural networks (CNN) to identify motor unit (MU) spike trains and estimate the neural drive to muscles from high-density electromyography (HD-EMG) signals in real time. Two distinct deep CNNs are compared with the convolution kernel compensation (CKC) algorithm using simulated and experimentally recorded signals. The effects of window size and step size of the input HD-EMG signals are also investigated. Approach. The MU spike trains were first identified with the CKC algorithm. The HD-EMG signals and spike trains were used to train the deep CNN. Then, the deep CNN decomposed the HD-EMG signals into MU discharge times in real time. Two CNN approaches are compared with the CKC: (a) multiple single-output deep CNN (SO-DCNN) with one MU decomposed per network, and (b) one multiple-output deep CNN (MO-DCNN) to decompose all MUs (up to 23) with one network. Main results. The MO-DCNN outperformed the SO-DCNN in terms of training time (3.2-21.4 s epoch −1 vs 6.5-47.8 s epoch −1 , respectively) and prediction time (0.04 vs 0.27 s sample −1 , respectively). The optimal window size and step size for MO-DCNN were 120 and 20 data points, respectively. It results in sensitivity of 98% and 85% with simulated and experimentally recorded HD-EMG signals, respectively.There is a high cross-correlation coefficient between the neural drive estimated with CKC and that estimated with MO-DCNN (range of r-value across conditions: 0.88-0.95). Significance. We demonstrate the feasibility and the validity of using deep CNN to accurately identify MU activity from HD-EMG with a latency lower than 80 ms, which falls within the lower bound of the human electromechanical delay. This method opens many opportunities for using the neural drive to interface humans with assistive devices.

show abstract

“…Territories were evenly distributed across the field of view (Fig. 4B), similar to the approach of farthest point sampling [10].…”

Section: Methodsmentioning

confidence: 99%

Identification of motor unit discharges from ultrasound images: Analysis of in silico and in vivo experiments

Rohlén,

Lubel,

Farina

2024

Preprint

Self Cite

View full text Add to dashboard Cite

Objective: Ultrasound (US) images during a muscle contraction can be decoded into individual motor unit (MU) activity, i.e., trains of neural discharges from the spinal cord. However, current decoding algorithms assume a stationary mixing matrix, i.e. equal mechanical twitches at each discharge. This study aimed to investigate the accuracy of these approaches in non-ideal conditions when the mechanical twitches in response to neural discharges vary over time and are partially fused in tetanic contractions. Methods: We performed an in silico experiment to study the decomposition accuracy for changes in simulation parameters, including the twitch waveforms, spatial territories, and motoneuron-driven activity. Then, we explored the consistency of the in silico findings with an in vivo experiment on the tibialis anterior muscle at varying contraction forces. Results: A large population of MU spike trains across different excitatory drives, and noise levels could be identified. The identified MUs with varying twitch waveforms resulted in varying amplitudes of the estimated sources correlated with the ground truth twitch amplitudes. The identified spike trains had a wide range of firing rates, and the later recruited MUs with larger twitch amplitudes were easier to identify than those with small amplitudes. Finally, the in silico and in vivo results were consistent, and the method could identify MU spike trains in US images at least up to 40% of the maximal voluntary contraction force. Conclusion: The decoding method was accurate irrespective of the varying twitch-like shapes or the degree of twitch fusion, indicating robustness, important for neural interfacing applications.

show abstract

Simulation of Motor Unit Action Potential Recordings From Intramuscular Multichannel Scanning Electrodes

Cited by 23 publications

References 43 publications

Larger and denser: an optimal design for surface grids of EMG electrodes to identify greater and more representative samples of motor units

Larger and denser: an optimal design for surface grids of EMG electrodes to identify greater and more representative samples of motor units

A convolutional neural network to identify motor units from high-density surface electromyography signals in real time

Identification of motor unit discharges from ultrasound images: Analysis of in silico and in vivo experiments

Contact Info

Product

Resources

About