Adaptive Spatial Filtering of High-Density EMG for Reducing the Influence of Noise and Artefacts in Myoelectric Control

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 muscl...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering 2020-07, Vol.28 (7), p.1511-1517
Main Authors: Stachaczyk, Martyna, Atashzar, S. Farokh, Farina, Dario
Format: Article
Language:English
Subjects:
Adaptive algorithms
Adaptive filters
Adaptive systems
Artefact detection
Attenuation
Automatic control
Density
Electric contacts
Electrodes
Electromyography
Filtering
Fingers
Frequency
Frequency measurement
LDA
multichannel adaptation
Muscles
Myoelectric control
Myoelectricity
Noise
noise attenuation
Noise reduction
Prosthetics
Spatial discrimination
Spatial filtering
Spatial resolution
Temporal variations
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Summary: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.
ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2020.2986099