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{Stachaczyk:2020:10.1109/TNSRE.2020.2986099,
author = {Stachaczyk, M and Atashzar, SF and Farina, D},
doi = {10.1109/TNSRE.2020.2986099},
journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
pages = {1511--1517},
title = {Adaptive spatial filtering of high-density EMG for reducing the influence of noise and artefacts in myoelectric control},
url = {http://dx.doi.org/10.1109/TNSRE.2020.2986099},
volume = {28},
year = {2020}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Electromyography (EMG) is a source of neural information for controlling neuroprosthetic devices. To enhance the information content of conventional bipolar EMG, high-density EMG systems include tens to hundreds of closely spaced electrodes that non-invasively record the electrical activity of muscles with high spatial resolution. Despite the advantages of relying on multiple signal sources, however, variations in electrode-skin contact impedance and noise remain challenging for multichannel myocontrol systems. These spatial and temporal non-stationarities negatively impact the control accuracy and therefore substantially limit the clinical viability of high-density EMG techniques. Here, we propose an adaptive algorithm for automatic artefact/noise detection and attenuation for high-density EMG control. The method infers the presence of noise in each EMG channel by spectro-temporal measures of signal similarity. These measures are then used for establishing a scoring system based on an adaptive weighting and reinforcement formulation. The method was experimentally tested as a pre-processing step for a multi-class discrimination problem of 4-digit activation. The approach was proven to enhance the discriminative information content of high-density EMG signals, as well as to attenuate non-stationary artefacts, with improvements in accuracy and robustness of the classification.
AU  - Stachaczyk,M
AU  - Atashzar,SF
AU  - Farina,D
DO  - 10.1109/TNSRE.2020.2986099
EP  - 1517
PY  - 2020///
SN  - 1534-4320
SP  - 1511
TI  - Adaptive spatial filtering of high-density EMG for reducing the influence of noise and artefacts in myoelectric control
T2  - IEEE Transactions on Neural Systems and Rehabilitation Engineering
UR  - http://dx.doi.org/10.1109/TNSRE.2020.2986099
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000546879800002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://ieeexplore.ieee.org/document/9093072
VL  - 28
ER  -
Download