ICA-based procedures for removing ballistocardiogram artifacts from EEG data acquired in the MRI scanner.

Details

Serval ID
serval:BIB_23117035D79E
Type
Article: article from journal or magazin.
Collection
Publications
Title
ICA-based procedures for removing ballistocardiogram artifacts from EEG data acquired in the MRI scanner.
Journal
NeuroImage
Author(s)
Srivastava G., Crottaz-Herbette S., Lau K.M., Glover G.H., Menon V.
ISSN
1053-8119 (Print)
ISSN-L
1053-8119
Publication state
Published
Issued date
01/01/2005
Peer-reviewed
Oui
Volume
24
Number
1
Pages
50-60
Language
english
Notes
Publication types: Journal Article ; Research Support, U.S. Gov't, P.H.S.
Publication Status: ppublish
Abstract
Electroencephalogram (EEG) data acquired in the MRI scanner contains significant artifacts, one of the most prominent of which is ballistocardiogram (BCG) artifact. BCG artifacts are generated by movement of EEG electrodes inside the magnetic field due to pulsatile changes in blood flow tied to the cardiac cycle. Independent Component Analysis (ICA) is a statistical algorithm that is useful for removing artifacts that are linearly and independently mixed with signals of interest. Here, we demonstrate and validate the usefulness of ICA in removing BCG artifacts from EEG data acquired in the MRI scanner. In accordance with our hypothesis that BCG artifacts are physiologically independent from EEG, it was found that ICA consistently resulted in five to six independent components representing the BCG artifact. Following removal of these components, a significant reduction in spectral power at frequencies associated with the BCG artifact was observed. We also show that our ICA-based procedures perform significantly better than noise-cancellation methods that rely on estimation and subtraction of averaged artifact waveforms from the recorded EEG. Additionally, the proposed ICA-based method has the advantage that it is useful in situations where ECG reference signals are corrupted or not available.
Keywords
Adult, Algorithms, Artifacts, Ballistocardiography/statistics & numerical data, Cerebral Cortex/physiology, Electroencephalography/statistics & numerical data, Female, Fourier Analysis, Humans, Linear Models, Magnetic Resonance Imaging/statistics & numerical data, Male, Mathematical Computing, Myocardial Contraction/physiology, Principal Component Analysis, Pulsatile Flow/physiology, Reproducibility of Results, Signal Processing, Computer-Assisted/instrumentation, Statistics as Topic
Pubmed
Web of science
Create date
15/02/2019 14:30
Last modification date
20/08/2019 13:00
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