Literature DB >> 23466939

Ballistocardiographic artifact removal from simultaneous EEG-fMRI using an optical motion-tracking system.

Pierre LeVan1, Julian Maclaren2, Michael Herbst3, Rebecca Sostheim3, Maxim Zaitsev3, Jürgen Hennig3.   

Abstract

The combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allows the investigation of neuronal activity with high temporal and spatial resolution. While much progress has been made to overcome the multiple technical challenges associated with the recording of EEG inside the MR scanner, the ballistocardiographic (BCG) artifact, which is caused by cardiac-related motion inside the magnetic field, remains a major issue affecting EEG quality. The BCG is difficult to remove by standard average artifact subtraction (AAS) methods due to its variability across cardiac cycles. We thus investigate the possibility of directly recording the BCG motion using an optical motion-tracking system. In 5 subjects, the system is shown to accurately measure BCG motion. Regressing out linear and quadratic functions of the measured motion parameters resulted in a significant reduction (p<0.05) in root-mean-square (RMS) amplitudes across cardiac cycles compared to AAS. A further significant RMS reduction was obtained when applying the regression and AAS methods sequentially, resulting in RMS amplitudes that were not significantly different from those of EEG recorded outside the scanner, although with higher residual variability. The large contributions of pure translational parameters and of non-linear terms to the BCG waveforms indicate that non-rigid motion of the EEG wires (originating from rigid head motion) is likely an important cause of the artifact.
Copyright © 2013 Elsevier Inc. All rights reserved.

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Year:  2013        PMID: 23466939     DOI: 10.1016/j.neuroimage.2013.02.039

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


  17 in total

Review 1.  Prospective motion correction in functional MRI.

Authors:  Maxim Zaitsev; Burak Akin; Pierre LeVan; Benjamin R Knowles
Journal:  Neuroimage       Date:  2016-11-11       Impact factor: 6.556

Review 2.  Use of EEG to diagnose ADHD.

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3.  Contact-free physiological monitoring using a markerless optical system.

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4.  Unified Retrospective EEG Motion Educated Artefact Suppression for EEG-fMRI to Suppress Magnetic Field Gradient Artefacts During Motion.

Authors:  Danilo Maziero; Victor A Stenger; David W Carmichael
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5.  Ballistocardiogram artifact removal with a reference layer and standard EEG cap.

Authors:  Qingfei Luo; Xiaoshan Huang; Gary H Glover
Journal:  J Neurosci Methods       Date:  2014-06-22       Impact factor: 2.390

6.  Removing Cardiac Artefacts in Magnetoencephalography with Resampled Moving Average Subtraction.

Authors:  Limin Sun; Seppo P Ahlfors; Hermann Hinrichs
Journal:  Brain Topogr       Date:  2016-08-08       Impact factor: 3.020

Review 7.  Simultaneous EEG and fMRI: towards the characterization of structure and dynamics of brain networks.

Authors:  Christoph Mulert
Journal:  Dialogues Clin Neurosci       Date:  2013-09       Impact factor: 5.986

8.  Negative BOLD in default-mode structures measured with EEG-MREG is larger in temporal than extra-temporal epileptic spikes.

Authors:  Julia Jacobs; Antonia Menzel; Georgia Ramantani; Katharina Körbl; Jakob Assländer; Andreas Schulze-Bonhage; Jürgen Hennig; Pierre LeVan
Journal:  Front Neurosci       Date:  2014-11-18       Impact factor: 4.677

9.  DeepIED: An epileptic discharge detector for EEG-fMRI based on deep learning.

Authors:  Yongfu Hao; Hui Ming Khoo; Nicolas von Ellenrieder; Natalja Zazubovits; Jean Gotman
Journal:  Neuroimage Clin       Date:  2017-12-05       Impact factor: 4.881

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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