Reference-free removal of EEG-fMRI ballistocardiogram artifacts with harmonic regression
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Author(s)
Bonmassar, Giorgio
Poulsen, Catherine
Pierce, Eric T.
Purdon, Patrick L.
Krishnaswamy, Pavitra
Brown, Emery Neal
Date Issued
July 2015
Journal
NeuroImage
Publisher
Elsevier
Citation
Krishnaswamy, Pavitra et al. “Reference-Free Removal of EEG-fMRI Ballistocardiogram Artifacts with Harmonic Regression.” NeuroImage 128 (March 2016): 398–412 © 2015 Elsevier Inc
Version
Author's final manuscript
Abstract
Combining electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) offers the potential for imaging brain activity with high spatial and temporal resolution. This potential remains limited by the significant ballistocardiogram (BCG) artifacts induced in the EEG by cardiac pulsation-related head movement within the magnetic field. We model the BCG artifact using a harmonic basis, pose the artifact removal problem as a local harmonic regression analysis, and develop an efficient maximum likelihood algorithm to estimate and remove BCG artifacts. Our analysis paradigm accounts for time-frequency overlap between the BCG artifacts and neurophysiologic EEG signals, and tracks the spatiotemporal variations in both the artifact and the signal. We evaluate performance on: simulated oscillatory and evoked responses constructed with realistic artifacts; actual anesthesia-induced oscillatory recordings; and actual visual evoked potential recordings. In each case, the local harmonic regression analysis effectively removes the BCG artifacts, and recovers the neurophysiologic EEG signals. We further show that our algorithm outperforms commonly used reference-based and component analysis techniques, particularly in low SNR conditions, the presence of significant time-frequency overlap between the artifact and the signal, and/or large spatiotemporal variations in the BCG. Because our algorithm does not require reference signals and has low computational complexity, it offers a practical tool for removing BCG artifacts from EEG data recorded in combination with fMRI.
MIT Department
Institute for Medical Engineering and Science
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1016/J.NEUROIMAGE.2015.06.088
