Article

Neuroinformatics

, Volume 8, Issue 2, pp 135-150

Removal of Muscle Artifacts from EEG Recordings of Spoken Language Production

  • De Maarten VosAffiliated withDepartment of Electrical Engineering (ESAT), Katholieke Universiteit Leuven Email author 
  • , Stephanie RièsAffiliated withLaboratoire de Psychologie Cognitive, Aix-Marseille Université, CNRSLaboratoire de Neurobiologie de la Cognition, Aix-Marseille Université, CNRS
  • , Katrien VanderperrenAffiliated withDepartment of Electrical Engineering (ESAT), Katholieke Universiteit Leuven
  • , Bart VanrumsteAffiliated withDepartment of Electrical Engineering (ESAT), Katholieke Universiteit LeuvenMOBILAB, Katholieke Hogeschool Kempen
  • , Francois-Xavier AlarioAffiliated withLaboratoire de Psychologie Cognitive, Aix-Marseille Université, CNRS
  • , Van Sabine HuffelAffiliated withDepartment of Electrical Engineering (ESAT), Katholieke Universiteit Leuven
  • , Boris BurleAffiliated withLaboratoire de Neurobiologie de la Cognition, Aix-Marseille Université, CNRS

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Abstract

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.

Keywords

EEG ERP EMG Artifact BSS Speech production