Imperial College London
Portrait Picture

ProfessorDarioFarina

Faculty of Engineering, Department of Bioengineering

Chair in Neurorehabilitation Engineering
 
 
 
//

Contact

 

+44 (0)20 7594 1387d.farina Website

 
 
//

Location

 

RSM 4.15Royal School of MinesSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Chen:2020:10.1109/tbme.2020.2989311,
author = {Chen, C and Ma, S and Sheng, X and Farina, D and Zhu, X},
doi = {10.1109/tbme.2020.2989311},
journal = {IEEE Transactions on Biomedical Engineering},
pages = {3501--3509},
title = {Adaptive real-time identification of motor unit discharges from non-stationary high-density surface electromyographic signals},
url = {http://dx.doi.org/10.1109/tbme.2020.2989311},
volume = {67},
year = {2020}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Objective. Estimation of the discharge pattern of motor units by electromyography (EMG) decomposition has been applied for neurophysiologic investigations, clinical diagnosis, and human-machine interfacing. However, most of the methods for EMG decomposition are currently applied offline. Here, we propose an approach for high-density surface EMG decomposition in real-time. Methods. A real-time decomposition scheme including two sessions, offline training and online decomposition, is proposed based on the convolutional kernel compensation algorithm. The estimation parameters, separation vectors and the thresholds for spike extraction, are first computed during offline training, and then they are directly applied to estimate motor unit spike trains (MUSTs) during the online decomposition. The estimation parameters are updated with the identification of new discharges to adapt to non-stationary conditions. The decomposition accuracy was validated on simulated EMG signals by convolving synthetic MUSTs with motor unit action potentials (MUAPs). Moreover, the accuracy of the online decomposition was assessed from experimental signals recorded from forearm muscles using a signal-based performance metrics (pulse-to-noise ratio, PNR). Main results. The proposed algorithm yielded a high decomposition accuracy and robustness to non-stationary conditions. The accuracy of MUSTs identified from simulated EMG signals was > 80% for most conditions. From experimental EMG signals, on average, 12±2 MUSTs were identified from each electrode grid with PNR of 25.0±1.8 dB, corresponding to an estimated decomposition accuracy > 75%. Conclusion and Significance. These results indicate the feasibility of real-time identification of motor unit activities non-invasively during variable force contractions, extending the potential applications of high-density EMG as a neural interface.
AU  - Chen,C
AU  - Ma,S
AU  - Sheng,X
AU  - Farina,D
AU  - Zhu,X
DO  - 10.1109/tbme.2020.2989311
EP  - 3509
PY  - 2020///
SN  - 0018-9294
SP  - 3501
TI  - Adaptive real-time identification of motor unit discharges from non-stationary high-density surface electromyographic signals
T2  - IEEE Transactions on Biomedical Engineering
UR  - http://dx.doi.org/10.1109/tbme.2020.2989311
UR  - https://ieeexplore.ieee.org/document/9075399
UR  - http://hdl.handle.net/10044/1/82742
VL  - 67
ER  -
Download