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Adaptive optimal basis set for BCG artifact removal in simultaneous EEG-fMRI

Electroencephalography (EEG) signals recorded during simultaneous functional magnetic resonance imaging (fMRI) are contaminated by strong artifacts. Among these, the ballistocardiographic (BCG) artifact is the most challenging, due to its complex spatio-temporal dynamics associated with ongoing card...

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Autores principales: Marino, Marco, Liu, Quanying, Koudelka, Vlastimil, Porcaro, Camillo, Hlinka, Jaroslav, Wenderoth, Nicole, Mantini, Dante
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5995808/
https://www.ncbi.nlm.nih.gov/pubmed/29891929
http://dx.doi.org/10.1038/s41598-018-27187-6
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author Marino, Marco
Liu, Quanying
Koudelka, Vlastimil
Porcaro, Camillo
Hlinka, Jaroslav
Wenderoth, Nicole
Mantini, Dante
author_facet Marino, Marco
Liu, Quanying
Koudelka, Vlastimil
Porcaro, Camillo
Hlinka, Jaroslav
Wenderoth, Nicole
Mantini, Dante
author_sort Marino, Marco
collection PubMed
description Electroencephalography (EEG) signals recorded during simultaneous functional magnetic resonance imaging (fMRI) are contaminated by strong artifacts. Among these, the ballistocardiographic (BCG) artifact is the most challenging, due to its complex spatio-temporal dynamics associated with ongoing cardiac activity. The presence of BCG residuals in EEG data may hide true, or generate spurious correlations between EEG and fMRI time-courses. Here, we propose an adaptive Optimal Basis Set (aOBS) method for BCG artifact removal. Our method is adaptive, as it can estimate the delay between cardiac activity and BCG occurrence on a beat-to-beat basis. The effective creation of an optimal basis set by principal component analysis (PCA) is therefore ensured by a more accurate alignment of BCG occurrences. Furthermore, aOBS can automatically estimate which components produced by PCA are likely to be BCG artifact-related and therefore need to be removed. The aOBS performance was evaluated on high-density EEG data acquired with simultaneous fMRI in healthy subjects during visual stimulation. As aOBS enables effective reduction of BCG residuals while preserving brain signals, we suggest it may find wide application in simultaneous EEG-fMRI studies.
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spelling pubmed-59958082018-06-21 Adaptive optimal basis set for BCG artifact removal in simultaneous EEG-fMRI Marino, Marco Liu, Quanying Koudelka, Vlastimil Porcaro, Camillo Hlinka, Jaroslav Wenderoth, Nicole Mantini, Dante Sci Rep Article Electroencephalography (EEG) signals recorded during simultaneous functional magnetic resonance imaging (fMRI) are contaminated by strong artifacts. Among these, the ballistocardiographic (BCG) artifact is the most challenging, due to its complex spatio-temporal dynamics associated with ongoing cardiac activity. The presence of BCG residuals in EEG data may hide true, or generate spurious correlations between EEG and fMRI time-courses. Here, we propose an adaptive Optimal Basis Set (aOBS) method for BCG artifact removal. Our method is adaptive, as it can estimate the delay between cardiac activity and BCG occurrence on a beat-to-beat basis. The effective creation of an optimal basis set by principal component analysis (PCA) is therefore ensured by a more accurate alignment of BCG occurrences. Furthermore, aOBS can automatically estimate which components produced by PCA are likely to be BCG artifact-related and therefore need to be removed. The aOBS performance was evaluated on high-density EEG data acquired with simultaneous fMRI in healthy subjects during visual stimulation. As aOBS enables effective reduction of BCG residuals while preserving brain signals, we suggest it may find wide application in simultaneous EEG-fMRI studies. Nature Publishing Group UK 2018-06-11 /pmc/articles/PMC5995808/ /pubmed/29891929 http://dx.doi.org/10.1038/s41598-018-27187-6 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Marino, Marco
Liu, Quanying
Koudelka, Vlastimil
Porcaro, Camillo
Hlinka, Jaroslav
Wenderoth, Nicole
Mantini, Dante
Adaptive optimal basis set for BCG artifact removal in simultaneous EEG-fMRI
title Adaptive optimal basis set for BCG artifact removal in simultaneous EEG-fMRI
title_full Adaptive optimal basis set for BCG artifact removal in simultaneous EEG-fMRI
title_fullStr Adaptive optimal basis set for BCG artifact removal in simultaneous EEG-fMRI
title_full_unstemmed Adaptive optimal basis set for BCG artifact removal in simultaneous EEG-fMRI
title_short Adaptive optimal basis set for BCG artifact removal in simultaneous EEG-fMRI
title_sort adaptive optimal basis set for bcg artifact removal in simultaneous eeg-fmri
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5995808/
https://www.ncbi.nlm.nih.gov/pubmed/29891929
http://dx.doi.org/10.1038/s41598-018-27187-6
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