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Abstract
For the analysis of simultaneous EEG-fMRI recordings, it is vital to use effective artifact removal tools. This applies in particular to the ballistocardiogram (BCG) artifact which is difficult to remove without distorting signals of interest related to brain activity. Here, we documented the use of surrogate source models to separate the artifact-related signals from brain signals with minimal distortion of the brain activity of interest. The artifact topographies used for surrogate separation were created automatically using principal components analysis (PCA-S) or by manual selection of artifact components utilizing independent components analysis (ICA-S). Using real resting-state data from 55 subjects superimposed with simulated auditory evoked potentials (AEP), both approaches were compared with three established BCG artifact removal methods: Blind Source Separation (BSS), Optimal Basis Set (OBS), and a mixture of both (OBS-ICA). Each method was evaluated for its applicability for ERP and source analysis using the following criteria: the number of events surviving artifact threshold scans, signal-to-noise ratio (SNR), error of source localization, and signal variance explained by the dipolar model. Using these criteria, PCA-S and ICA-S fared best overall, with highly significant differences to the established methods, especially in source localization. The PCA-S approach was also applied to a single subject Berger experiment performed in the MRI scanner. Overall, the removal of BCG artifacts by the surrogate methods provides a substantial improvement for the analysis of simultaneous EEG-fMRI data compared to the established methods.
Highlights
Interest in simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging experiments has grown, ever since Logothetis et al (2001) showed a clear relationship between EEG and the blood oxygenation level-dependent (BOLD) signal
The highest mean value was observed for the principal components analysis (PCA-S) method (x = 178 ± 16)
Lower values were obtained for independent components analysis (ICA-S) (x = 155 ± 41), Optimal Basis Set (OBS) (x = 120 ± 44) and OBS-independent component analysis (ICA) (x = 126 ± 45) while the Blind Source Separation (BSS) method showed the lowest numbers (x = 93 ± 62)
Summary
Interest in simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) experiments has grown, ever since Logothetis et al (2001) showed a clear relationship between EEG and the blood oxygenation level-dependent (BOLD) signal. The utility of simultaneous EEG-fMRI recordings is limited by three main interconnected factors: (1) the effectiveness of fMRI-related artifact reduction from EEG recording; (2) the usability of analytical tools; (3) the examination cost. Two types of artifacts are predominant in the EEG signal recorded during fMRI acquisition. The first type is an imaging artifact induced by the magnetic gradient coils (Allen et al, 2000). The second type, the so-called ballistocardiogram (BCG), is related to the heartbeat (Debener et al, 2008) or pulse artifact (Yan et al, 2010). The BCG artifact can vary over the duration of a recording (Marino et al, 2018a) because of various factors (i.e., position change in MRI, blood pressure change, etc.)
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