TY - JOUR
T1 - Adaptive Spatial Filtering of High-Density EMG for Reducing the Influence of Noise and Artefacts in Myoelectric Control
AU - Stachaczyk, Martyna
AU - Farokh Atashzar, S.
AU - Farina, Dario
N1 - Funding Information:
Manuscript received October 18, 2019; revised February 26, 2020; accepted March 16, 2020. Date of publication May 14, 2020; date of current version July 8, 2020. This work was supported in part by the European Research Council Synergy project Natural BionicS under Grant 810346. (Corresponding author: Dario Farina.) Martyna Stachaczyk and Dario Farina are with the Department of Bioengineering, Imperial College London, London SW7 2AZ, U.K. (e-mail: [email protected]; [email protected]).
Publisher Copyright:
© 2001-2011 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - 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.
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.
KW - Artefact detection
KW - LDA
KW - electromyography
KW - multichannel adaptation
KW - noise attenuation
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U2 - 10.1109/TNSRE.2020.2986099
DO - 10.1109/TNSRE.2020.2986099
M3 - Article
C2 - 32406842
AN - SCOPUS:85088107185
SN - 1534-4320
VL - 28
SP - 1511
EP - 1517
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
IS - 7
M1 - 9093072
ER -