Literature DB >> 27012501

Ballistocardiogram artifact correction taking into account physiological signal preservation in simultaneous EEG-fMRI.

Rodolfo Abreu1, Marco Leite2, João Jorge3, Frédéric Grouiller4, Wietske van der Zwaag5, Alberto Leal6, Patrícia Figueiredo7.   

Abstract

The ballistocardiogram (BCG) artifact is currently one of the most challenging in the EEG acquired concurrently with fMRI, with correction invariably yielding residual artifacts and/or deterioration of the physiological signals of interest. In this paper, we propose a family of methods whereby the EEG is decomposed using Independent Component Analysis (ICA) and a novel approach for the selection of BCG-related independent components (ICs) is used (PROJection onto Independent Components, PROJIC). Three ICA-based strategies for BCG artifact correction are then explored: 1) BCG-related ICs are removed from the back-reconstruction of the EEG (PROJIC); and 2-3) BCG-related ICs are corrected for the artifact occurrences using an Optimal Basis Set (OBS) or Average Artifact Subtraction (AAS) framework, before back-projecting all ICs onto EEG space (PROJIC-OBS and PROJIC-AAS, respectively). A novel evaluation pipeline is also proposed to assess the methods performance, which takes into account not only artifact but also physiological signal removal, allowing for a flexible weighting of the importance given to physiological signal preservation. This evaluation is used for the group-level parameter optimization of each algorithm on simultaneous EEG-fMRI data acquired using two different setups at 3T and 7T. Comparison with state-of-the-art BCG correction methods showed that PROJIC-OBS and PROJIC-AAS outperformed the others when priority was given to artifact removal or physiological signal preservation, respectively, while both PROJIC-AAS and AAS were in general the best choices for intermediate trade-offs. The impact of the BCG correction on the quality of event-related potentials (ERPs) of interest was assessed in terms of the relative reduction of the standard error (SE) across trials: 26/66%, 32/62% and 18/61% were achieved by, respectively, PROJIC, PROJIC-OBS and PROJIC-AAS, for data collected at 3T/7T. Although more significant improvements were achieved at 7T, the results were qualitatively comparable for both setups, which indicate the wide applicability of the proposed methodologies and recommendations.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Ballistocardiogram; Electroencephalography; Independent Component Analysis; fMRI

Mesh:

Year:  2016        PMID: 27012501     DOI: 10.1016/j.neuroimage.2016.03.034

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  11 in total

1.  Improved 7 Tesla resting-state fMRI connectivity measurements by cluster-based modeling of respiratory volume and heart rate effects.

Authors:  Joana Pinto; Sandro Nunes; Marta Bianciardi; Afonso Dias; L Miguel Silveira; Lawrence L Wald; Patrícia Figueiredo
Journal:  Neuroimage       Date:  2017-04-06       Impact factor: 6.556

2.  Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF).

Authors:  David Steyrl; Gunther Krausz; Karl Koschutnig; Günter Edlinger; Gernot R Müller-Putz
Journal:  Brain Topogr       Date:  2017-11-09       Impact factor: 3.020

Review 3.  EEG-Informed fMRI: A Review of Data Analysis Methods.

Authors:  Rodolfo Abreu; Alberto Leal; Patrícia Figueiredo
Journal:  Front Hum Neurosci       Date:  2018-02-06       Impact factor: 3.169

4.  Identification of epileptic brain states by dynamic functional connectivity analysis of simultaneous EEG-fMRI: a dictionary learning approach.

Authors:  Rodolfo Abreu; Alberto Leal; Patrícia Figueiredo
Journal:  Sci Rep       Date:  2019-01-24       Impact factor: 4.379

5.  EEG-fMRI: Ballistocardiogram Artifact Reduction by Surrogate Method for Improved Source Localization.

Authors:  Mateusz Rusiniak; Harald Bornfleth; Jae-Hyun Cho; Tomasz Wolak; Nicole Ille; Patrick Berg; Michael Scherg
Journal:  Front Neurosci       Date:  2022-03-10       Impact factor: 4.677

6.  Clustering-Constrained ICA for Ballistocardiogram Artifacts Removal in Simultaneous EEG-fMRI.

Authors:  Kai Wang; Wenjie Li; Li Dong; Ling Zou; Changming Wang
Journal:  Front Neurosci       Date:  2018-02-13       Impact factor: 4.677

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

Authors:  Marco Marino; Quanying Liu; Vlastimil Koudelka; Camillo Porcaro; Jaroslav Hlinka; Nicole Wenderoth; Dante Mantini
Journal:  Sci Rep       Date:  2018-06-11       Impact factor: 4.379

8.  Exploring the relative efficacy of motion artefact correction techniques for EEG data acquired during simultaneous fMRI.

Authors:  Alexander J Daniel; James A Smith; Glyn S Spencer; João Jorge; Richard Bowtell; Karen J Mullinger
Journal:  Hum Brain Mapp       Date:  2018-10-19       Impact factor: 5.038

Review 9.  Simultaneous EEG-fMRI: What Have We Learned and What Does the Future Hold?

Authors:  Tracy Warbrick
Journal:  Sensors (Basel)       Date:  2022-03-15       Impact factor: 3.576

10.  Data-driven beamforming technique to attenuate ballistocardiogram artefacts in electroencephalography-functional magnetic resonance imaging without detecting cardiac pulses in electrocardiography recordings.

Authors:  Makoto Uji; Nathan Cross; Florence B Pomares; Aurore A Perrault; Aude Jegou; Alex Nguyen; Umit Aydin; Jean-Marc Lina; Thien Thanh Dang-Vu; Christophe Grova
Journal:  Hum Brain Mapp       Date:  2021-06-08       Impact factor: 5.038

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