, Volume 8, Issue 2, pp 135–150 | Cite as

Removal of Muscle Artifacts from EEG Recordings of Spoken Language Production

  • De Maarten VosEmail author
  • Stephanie Riès
  • Katrien Vanderperren
  • Bart Vanrumste
  • Francois-Xavier Alario
  • Van Sabine Huffel
  • Boris Burle


Research on the neural basis of language processing has often avoided investigating spoken language production by fear of the electromyographic (EMG) artifacts that articulation induces on the electro-encephalogram (EEG) signal. Indeed, such articulation artifacts are typically much larger than the brain signal of interest. Recently, a Blind Source Separation technique based on Canonical Correlation Analysis was proposed to separate tonic muscle artifacts from continuous EEG recordings in epilepsy. In this paper, we show how the same algorithm can be adapted to remove the short EMG bursts due to articulation on every trial. Several analyses indicate that this method accurately attenuates the muscle contamination on the EEG recordings, providing to the neurolinguistic community a powerful tool to investigate the brain processes at play during overt language production.


EEG ERP EMG Artifact BSS Speech production 



This research is funded by a PhD grant of the Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Vlaanderen)and a doctoral grant for the French ministry of research; Research supported by ANR-07-JCJC-0074; Research Council KUL: GOA-AMBioRICS, GOA-MANET, CoE EF/05/006 Optimization in Engineering (OPTEC), IDO 05/010 EEG-fMRI, IOF-KP06/11 FunCopt, several PhD/postdoc & fellow grants; Flemish Government: FWO: PhD/postdoc grants, projects, G.0407.02 (support vector machines), G.0360.05 (EEG, Epileptic), G.0519.06 (Noninvasive brain oxygenation), G.0321.06 (Tensors/Spectral Analysis), G.0302.07 (SVM), G.0341.07 (Data fusion), G.0427.10N (Integrated EEG-fMRI), research communities (ICCoS, ANMMM); IWT: TBM070713-Accelero, TBM-IOTA3; Belgian Federal Science Policy Of\/f\/ice IUAP P6/04 (DYSCO, ‘Dynamical systems, control and optimization’, 2007–2011); EU: BIOPATTERN (FP6-2002-IST 508803), ETUMOUR (FP6-2002-LIFESCIHEALTH 503094), FAST (FP6-MC-RTN-035801), Neuromath (COST-BM0601) ESA: Cardiovascular Control (Prodex-8 C90242), European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013 Grant Agreement no. 241077)

Information Sharing Statement

The original BSS-CCA method is available at The proposed automatization can be obtained after sending an email to the corresponding author.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • De Maarten Vos
    • 1
    Email author
  • Stephanie Riès
    • 2
    • 3
  • Katrien Vanderperren
    • 1
  • Bart Vanrumste
    • 1
    • 4
  • Francois-Xavier Alario
    • 2
  • Van Sabine Huffel
    • 1
  • Boris Burle
    • 3
  1. 1.Department of Electrical Engineering (ESAT)Katholieke Universiteit LeuvenLeuvenBelgium
  2. 2.Laboratoire de Psychologie CognitiveAix-Marseille Université, CNRSMarseilleFrance
  3. 3.Laboratoire de Neurobiologie de la CognitionAix-Marseille Université, CNRSMarseilleFrance
  4. 4.MOBILABKatholieke Hogeschool KempenGeelBelgium

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