Literature DB >> 19457365

Removal of imaging artifacts in EEG during simultaneous EEG/fMRI recording: reconstruction of a high-precision artifact template.

Miika Koskinen1, Nuutti Vartiainen.   

Abstract

Functional magnetic resonance imaging (fMRI) induces coarse electromagnetic artifacts into the simultaneously recorded electroencephalogram (EEG). The problem in the signal processing framework is to model the underlying artifact, which is time-continuous, as a discretely sampled waveform. To build up an artifact template, the EEG sampling in relation to the phase of the imaging artifacts should be known. If the MR scanner and EEG sampling are not synchronized, this relation is not constant and a time adjustment of the template with the individual slice artifacts becomes essential. However, lack of synchrony opens up the possibility for approximating a high-precision and continuous artifact template by using the samples acquired from slightly different phases of the induced artifact. In this work, methodology for reconstructing such a template was developed using EEG data recorded simultaneously with fMRI at 3 T. A time-continuous cubic spline approximation was used as the slice artifact model. To overcome the problem of non-synchronized clocks, two methods were proposed to find the starting times of the slice artifacts at sub-sample precision. This approach yielded efficient imaging artifact reduction: the amplitude at the dominant frequency was attenuated by 55-70 dB (the median values over EEG channels) and the residual signal, at its best, was practically free from sharp transients even with 5000 Hz sampling frequency and without further residual artifact reduction algorithms. The presented methods may reduce the need for post-processing of the residual signal after the template subtraction and may help to preserve the EEG bandwidth.

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Year:  2009        PMID: 19457365     DOI: 10.1016/j.neuroimage.2009.01.061

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


  5 in total

1.  Coupling electrophysiological and hemodynamic responses to errors.

Authors:  Nuria Doñamayor; Urs Heilbronner; Thomas F Münte
Journal:  Hum Brain Mapp       Date:  2011-05-26       Impact factor: 5.038

2.  Validation of ICA-based myogenic artifact correction for scalp and source-localized EEG.

Authors:  Brenton W McMenamin; Alexander J Shackman; Jeffrey S Maxwell; David R W Bachhuber; Adam M Koppenhaver; Lawrence L Greischar; Richard J Davidson
Journal:  Neuroimage       Date:  2009-10-13       Impact factor: 6.556

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.  A New Generation of Brain-Computer Interfaces Driven by Discovery of Latent EEG-fMRI Linkages Using Tensor Decomposition.

Authors:  Gopikrishna Deshpande; D Rangaprakash; Luke Oeding; Andrzej Cichocki; Xiaoping P Hu
Journal:  Front Neurosci       Date:  2017-06-07       Impact factor: 4.677

5.  Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study.

Authors:  Niels Trusbak Haumann; Lauri Parkkonen; Marina Kliuchko; Peter Vuust; Elvira Brattico
Journal:  Comput Intell Neurosci       Date:  2016-07-21
  5 in total

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