Abstract
Ballistocardiogram (BCG) artifact remains a major challenge that renders electroencephalographic (EEG) signals hard to interpret in simultaneous EEG and functional MRI (fMRI) data acquisition. Here, we propose an integrated learning and inference approach that takes advantage of a commercial high-density EEG cap, to estimate the BCG contribution in noisy EEG recordings from inside the MR scanner. To estimate reliably the full-scalp BCG artifacts, a near-optimal subset (20 out of 256) of channels first was identified using a modified recording setup. In subsequent recordings inside the MR scanner, BCG-only signal from this subset of channels was used to generate continuous estimates of the full-scalp BCG artifacts via inference, from which the intended EEG signal was recovered. The reconstruction of the EEG was performed with both a direct subtraction and an optimization scheme. We evaluated the performance on both synthetic and real contaminated recordings, and compared it to the benchmark Optimal Basis Set (OBS) method. In the challenging non-event-related-potential (non-ERP) EEG studies, our reconstruction can yield more than fourteen-fold improvement in reducing the normalized RMS error of EEG signals, compared to OBS.
Highlights
Simultaneous electroencephalography and functional magnetic resonance imaging acquisition offers a promising probe to study different, yet connected, bioelectric and hemodynamic attributes of brain activity with complementary temporal and spatial resolutions
Ballistocardiogram (BCG) artifact in concurrent EEG-functional MRI (fMRI) acquisition still presents a challenge in continuous recoding studies especially when the magnetic field strength is high
The widely used Optimal Basis Sets method (OBS) (Niazy et al, 2005) is a principal component analysis (PCA)-based approach that regresses out the mean effects and its first few principal components from the contaminated data on a heartbeat-byheartbeat basis
Summary
Simultaneous electroencephalography and functional magnetic resonance imaging acquisition offers a promising probe to study different, yet connected, bioelectric and hemodynamic attributes of brain activity with complementary temporal and spatial resolutions. This non-invasive neuroimaging technique has applications in the analysis of event-related brain responses (Eichele et al, 2005; Debener et al, 2006; Benar et al, 2007), studies of ongoing brain rhythms and networks (Goldman et al, 2002; Laufs et al, 2003), and studies of epileptic activity (Krakow et al, 2001; Lemieux et al, 2001; Bénar et al, 2003). These PCA/ICA-based approaches are based on strong orthogonality/independence assumptions and subject to manual selection of number of components to be included
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