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Title: Removal of BCG artifacts from EEG recordings inside the MR scanner: A comparison of methodological and validation-related aspects
Authors: Vanderperren, Katrien ×
De Vos, Maarten
Ramautar, Jennifer R
Novitskiy, Nikolay
Mennes, Maarten
Assecondi, Sara
Vanrumste, Bart
Stiers, Peter
Van den Bergh, Bea
Wagemans, Johan
Lagae, Lieven
Sunaert, Stefan
Van Huffel, Sabine #
Issue Date: 2010
Publisher: Academic Press
Series Title: NeuroImage vol:50 issue:3 pages:920-934
Abstract: Multimodal approaches are of growing interest in the study of neural processes. To this end much attention has been paid to the integration of electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data because of their complementary properties. However, the simultaneous acquisition of both types of data causes serious artifacts in the EEG, with amplitudes that may be much larger than those of EEG signals themselves. The most challenging of these artifacts is the ballistocardiogram (BCG) artifact, caused by pulse-related electrode movements inside the magnetic field. Despite numerous efforts to find a suitable approach to remove this artifact, still a considerable discrepancy exists between current EEG-fMRI studies. This paper attempts to clarify several methodological issues regarding the different approaches with an extensive validation based on event-related potentials (ERPs). More specifically, Optimal Basis Set (OBS) and Independent Component Analysis (ICA) based methods were investigated. Their validation was not only performed with measures known from previous studies on the average ERPs, but most attention was focused on task-related measures, including their use on trial-to-trial information. These more detailed validation criteria enabled us to find a clearer distinction between the most widely used cleaning methods. Both OBS and ICA proved to be able to yield equally good results. However, ICA methods needed more parameter tuning, thereby making OBS more robust and easy to use. Moreover, applying OBS prior to ICA can optimize the data quality even more, but caution is recommended since the effect of the additional ICA step may be strongly subject-dependent.
URI: 
ISSN: 1053-8119
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Radiology
Brain & Metabolism Section (-)
Laboratory for Experimental Psychology
ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
Translational MRI (+)
Health Psychology
Technologiecluster ESAT Elektrotechnische Engineering
Electrical Engineering (ESAT) TC, Technology Campus Geel
Electrical Engineering (ESAT) TC, Technology Campus Diepenbeek
× corresponding author
# (joint) last author

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