Removal of BCG artifacts from EEG recordings inside the MR scanner: A comparison of methodological and validation-related aspects

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.

Pulse Artifact Detection in Simultaneous EEG–fMRI Recording Based on EEG Map Topography

“…The standardized strategy to correct PA relies on Average Artifact Subtraction, AAS (Allen et al 1998), which can be complemented by estimating an Optimal Basis Set of principal components (Niazy et al 2005) and/or by an ICA (Independent Component Analysis) decomposition to remove residual artifacts (Debener et al 2007;Vanderperren et al 2010). Other authors proposed a direct ICA on EEG (Benar et al 2003;Marques et al 2009).…”

Dual array EEG-fMRI: An approach for motion artifact suppression in EEG recorded simultaneously with fMRI

“…Manual selection was performed by Mantini et al (2007) and Huiskamp (2006). Vanderperren et al (2010) employed more complex criteria set for component selection, which was based on several parameters: correlation, frequency content, autocorrelation, variance and peak-to-peak amplitude of reconstructed sources. However, daEEG allows utilization of a simple principle for independent component selection: components whose spatial distribution is independent of EEG cable arrangements correspond with brain activity, while those that are influenced by EEG cable arrangements correspond with motion artifacts (either BCG or non-BCG).…”

“…For example, LeVan et al (2013) found distortions of the EEG data following OBS in the form of attenuation of alpha oscillations and Liu et al (2012) found that OBS induced spurious lowfrequency (3-8 Hz) components that were absent in the clean EEG spectrum. These distortions may be due to the suboptimal choice of the number of components used (LeVan et al, 2013) or suboptimal detection of QRS events (Vanderperren et al, 2010). Alternatively, this increase in peak-to-peak amplitude can also be a result of the hybrid procedure itself.…”

Characterisation and Reduction of the EEG Artefact Caused by the Helium Cooling Pump in the MR Environment: Validation in Epilepsy Patient Data

“…Another study investigated the effects of two common pulse artefact correction methods on the quality of auditory event-related potentials (ERP's) (Debener et al 2007). A more recent study attempted to validate pulse artefact correction methods by using detailed criteria based on the average ERP's as well as their trial-by-trial modulations (Vanderperren et al 2010). For the evaluation of the reduction of the gradient artefact, a method based on both the amount of artefact reduction and that of biosignal preservation, in different frequency bands, has been proposed (Freyer et al 2009).…”