Abstract
Despite considerable effort to remove it, the ballistocardiogram (BCG) remains a major artifact in electroencephalographic data (EEG) acquired inside magnetic resonance imaging (MRI) scanners, particularly in continuous (as opposed to event-related) recordings. In this study, we have developed a new Direct Recording Prior Encoding (DRPE) method to extract and separate the BCG and EEG components from contaminated signals, and have demonstrated its performance by comparing it quantitatively to the popular Optimal Basis Set (OBS) method. Our modified recording configuration allows us to obtain representative bases of the BCG- and EEG-only signals. Further, we have developed an optimization-based reconstruction approach to maximally incorporate prior knowledge of the BCG/EEG subspaces, and of the signal characteristics within them. Both OBS and DRPE methods were tested with experimental data, and compared quantitatively using cross-validation. In the challenging continuous EEG studies, DRPE outperforms the OBS method by nearly sevenfold in separating the continuous BCG and EEG signals.
Highlights
Concurrent acquisition of electroencephalographic data (EEG) and functional magnetic resonance imaging is an approach with great potential for studying different, yet connected aspects of cerebral activity, bioelectric and hemodynamic attributes
To evaluate different artifact removal approaches quantitatively, and to provide parameter selection guidance when real contaminated data is used, we simulated contaminated EEG data from known BCG-only and EEG-only recordings according to the generative model introduced in section 2, allowing direct comparison between reconstructed and ground-truth component signals
FROM REAL CONTAMINATED DATA One of the most robust effects on the EEG results from signal comparisons of eyes-closed (EC) and eyes-open (EO) states at rest, which results in large alpha band increases in the EC condition (Berger, 1929)
Summary
Concurrent acquisition of EEG and functional magnetic resonance imaging (fMRI) is an approach with great potential for studying different, yet connected aspects of cerebral activity, bioelectric and hemodynamic attributes. With their different temporal and spatial resolutions, EEG and fMRI are understood to be linked functionally, and yet to hold complementary information regarding underlying brain activity. The BCG presents high temporal non-stationarity due to variation in cardiac cycles (Bonmassar et al, 2002; Debener et al, 2007), and its amplitude scales with magnetic field strength (Yan et al, 2010; Mullinger et al, 2013)
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