Removal of muscular artifacts in EEG signals: a comparison of linear decomposition methods

Laura Frølich*, Irene Dowding

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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The most common approach to reduce muscle artifacts in electroencephalographic signals is to linearly decompose the signals in order to separate artifactual from neural sources, using one of several variants of independent component analysis (ICA). Here we compare three of the most commonly used ICA methods (extended Infomax, FastICA and TDSEP) with two other linear decomposition methods (Fourier-ICA and spatio-spectral decomposition) suitable for the extraction of oscillatory activity. We evaluate the methods’ ability to remove event-locked muscle artifacts while maintaining event-related desynchronization in data from 18 subjects who performed self-paced foot movements. We find that all five analyzed methods drastically reduce the muscle artifacts. For the three ICA methods, adequately high-pass filtering is very important. Compared to the effect of high-pass filtering, differences between the five analyzed methods were small, with extended Infomax performing best.

Original languageEnglish
JournalBrain Informatics
Issue number1
Pages (from-to)13-22
Publication statusPublished - 1 Mar 2018


  • Artifact removal
  • Blind source separation (BSS)
  • Electroencephalogram (EEG)
  • Filtering
  • Independent component analysis (ICA)
  • Muscle artifacts


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