Non-linear filter for gradient artefact correction during simultaneous EEG-fMRI

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Abstract: Parallel to the breakthroughs on the usage of simultaneous EEG-fMRI in neurocognitive studies and research, the occurrence of artefacts in the EEG signal induced within the fMRI scanner constitutes one of the challenges to be overcome in order to broaden the range of applications of such a technique. It is the case of the gradient artefact, provoked by the variation of gradient magnetic fields. Thereby, although a number of computational methodologies have yielded a satisfactory correction of the EEG signal, novel approaches have been proposed to improve the quality of the restored EEG. This work presents a novel proposal for modelling the variability of the gradient artefact template estimated during application of the established average artefact subtraction (AAS) method. Implementation of our proposed model and its combination with the AAS method allow an effective elimination of the gradient artefact from EEG recordings. Moreover, our approach performs an adaptive removal of the underlying artefact residuals, according to the signal slope parameter characteristics.
Original languageEnglish
Pages (from-to)55-63
Number of pages9
JournalSignal Processing Research
Volume2
Issue number3
Publication statusPublished - 2013

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Electroencephalography
Magnetic Resonance Imaging
Magnetic fields

Cite this

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title = "Non-linear filter for gradient artefact correction during simultaneous EEG-fMRI",
abstract = "Abstract: Parallel to the breakthroughs on the usage of simultaneous EEG-fMRI in neurocognitive studies and research, the occurrence of artefacts in the EEG signal induced within the fMRI scanner constitutes one of the challenges to be overcome in order to broaden the range of applications of such a technique. It is the case of the gradient artefact, provoked by the variation of gradient magnetic fields. Thereby, although a number of computational methodologies have yielded a satisfactory correction of the EEG signal, novel approaches have been proposed to improve the quality of the restored EEG. This work presents a novel proposal for modelling the variability of the gradient artefact template estimated during application of the established average artefact subtraction (AAS) method. Implementation of our proposed model and its combination with the AAS method allow an effective elimination of the gradient artefact from EEG recordings. Moreover, our approach performs an adaptive removal of the underlying artefact residuals, according to the signal slope parameter characteristics.",
author = "J.L. Ferreira and P.J.M. Cluitmans and R.M. Aarts",
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Non-linear filter for gradient artefact correction during simultaneous EEG-fMRI. / Ferreira, J.L.; Cluitmans, P.J.M.; Aarts, R.M.

In: Signal Processing Research, Vol. 2, No. 3, 2013, p. 55-63.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Non-linear filter for gradient artefact correction during simultaneous EEG-fMRI

AU - Ferreira, J.L.

AU - Cluitmans, P.J.M.

AU - Aarts, R.M.

PY - 2013

Y1 - 2013

N2 - Abstract: Parallel to the breakthroughs on the usage of simultaneous EEG-fMRI in neurocognitive studies and research, the occurrence of artefacts in the EEG signal induced within the fMRI scanner constitutes one of the challenges to be overcome in order to broaden the range of applications of such a technique. It is the case of the gradient artefact, provoked by the variation of gradient magnetic fields. Thereby, although a number of computational methodologies have yielded a satisfactory correction of the EEG signal, novel approaches have been proposed to improve the quality of the restored EEG. This work presents a novel proposal for modelling the variability of the gradient artefact template estimated during application of the established average artefact subtraction (AAS) method. Implementation of our proposed model and its combination with the AAS method allow an effective elimination of the gradient artefact from EEG recordings. Moreover, our approach performs an adaptive removal of the underlying artefact residuals, according to the signal slope parameter characteristics.

AB - Abstract: Parallel to the breakthroughs on the usage of simultaneous EEG-fMRI in neurocognitive studies and research, the occurrence of artefacts in the EEG signal induced within the fMRI scanner constitutes one of the challenges to be overcome in order to broaden the range of applications of such a technique. It is the case of the gradient artefact, provoked by the variation of gradient magnetic fields. Thereby, although a number of computational methodologies have yielded a satisfactory correction of the EEG signal, novel approaches have been proposed to improve the quality of the restored EEG. This work presents a novel proposal for modelling the variability of the gradient artefact template estimated during application of the established average artefact subtraction (AAS) method. Implementation of our proposed model and its combination with the AAS method allow an effective elimination of the gradient artefact from EEG recordings. Moreover, our approach performs an adaptive removal of the underlying artefact residuals, according to the signal slope parameter characteristics.

M3 - Article

VL - 2

SP - 55

EP - 63

JO - Signal Processing Research

JF - Signal Processing Research

SN - 2327-1701

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ER -