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

Martyna Stachaczyk, S. Farokh Atashzar, Dario Farina

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number9093072
Pages (from-to)1511-1517
Number of pages7
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number7
StatePublished - Jul 2020


  • Artefact detection
  • LDA
  • electromyography
  • multichannel adaptation
  • noise attenuation

ASJC Scopus subject areas

  • Internal Medicine
  • General Neuroscience
  • Biomedical Engineering
  • Rehabilitation


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