Reference layer artefact subtraction (RLAS): A novel method of minimizing EEG artefacts during simultaneous fMRI

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Reference layer artefact subtraction (RLAS) : A novel method of minimizing EEG artefacts during simultaneous fMRI. / Chowdhury, Muhammad E H; Mullinger, Karen; Glover, Paul; Bowtell, Richard.

In: NeuroImage, Vol. 84, 01.01.2014, p. 307-319.

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Chowdhury, Muhammad E H ; Mullinger, Karen ; Glover, Paul ; Bowtell, Richard. / Reference layer artefact subtraction (RLAS) : A novel method of minimizing EEG artefacts during simultaneous fMRI. In: NeuroImage. 2014 ; Vol. 84. pp. 307-319.

Bibtex

@article{d6217725d1e541f59462c0b3d408ec31,
title = "Reference layer artefact subtraction (RLAS): A novel method of minimizing EEG artefacts during simultaneous fMRI",
abstract = "Large artefacts compromise EEG data quality during simultaneous fMRI. These artefact voltages pose heavy demands on the bandwidth and dynamic range of EEG amplifiers and mean that even small fractional variations in the artefact voltages give rise to significant residual artefacts after average artefact subtraction. Any intrinsic reduction in the magnitude of the artefacts would be highly advantageous, allowing data with a higher bandwidth to be acquired without amplifier saturation, as well as reducing the residual artefacts that can easily swamp signals from brain activity measured using current methods. Since these problems currently limit the utility of simultaneous EEG-fMRI, new approaches for reducing the magnitude and variability of the artefacts are required. One such approach is the use of an EEG cap that incorporates electrodes embedded in a reference layer that has similar conductivity to tissue and is electrically isolated from the scalp. With this arrangement, the artefact voltages produced on the reference layer leads by time-varying field gradients, cardiac pulsation and subject movement are similar to those induced in the scalp leads, but neuronal signals are not detected in the reference layer. Taking the difference of the voltages in the reference and scalp channels will therefore reduce the artefacts, without affecting sensitivity to neuronal signals. Here, we test this approach by using a simple experimental realisation of the reference layer to investigate the artefacts induced on the leads attached to the reference layer and scalp and to evaluate the degree of artefact attenuation that can be achieved via reference layer artefact subtraction (RLAS). Through a series of experiments on phantoms and human subjects, we show that RLAS significantly reduces the gradient (GA), pulse (PA) and motion (MA) artefacts, while allowing accurate recording of neuronal signals. The results indicate that RLAS generally outperforms AAS when motion is present in the removal of the GA and PA, while the combination of AAS and RLAS always produces higher artefact attenuation than AAS. Additionally, we demonstrate that RLAS greatly attenuates the unpredictable and highly variable MAs that are very hard to remove using post-processing methods.",
keywords = "Artefact correction, Artefact removal, Gradient artefact, Motion artefact, Pulse artefact, Simultaneous EEG-fMRI",
author = "Chowdhury, {Muhammad E H} and Karen Mullinger and Paul Glover and Richard Bowtell",
year = "2014",
month = jan,
day = "1",
doi = "10.1016/j.neuroimage.2013.08.039",
language = "English",
volume = "84",
pages = "307--319",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Reference layer artefact subtraction (RLAS)

T2 - A novel method of minimizing EEG artefacts during simultaneous fMRI

AU - Chowdhury, Muhammad E H

AU - Mullinger, Karen

AU - Glover, Paul

AU - Bowtell, Richard

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Large artefacts compromise EEG data quality during simultaneous fMRI. These artefact voltages pose heavy demands on the bandwidth and dynamic range of EEG amplifiers and mean that even small fractional variations in the artefact voltages give rise to significant residual artefacts after average artefact subtraction. Any intrinsic reduction in the magnitude of the artefacts would be highly advantageous, allowing data with a higher bandwidth to be acquired without amplifier saturation, as well as reducing the residual artefacts that can easily swamp signals from brain activity measured using current methods. Since these problems currently limit the utility of simultaneous EEG-fMRI, new approaches for reducing the magnitude and variability of the artefacts are required. One such approach is the use of an EEG cap that incorporates electrodes embedded in a reference layer that has similar conductivity to tissue and is electrically isolated from the scalp. With this arrangement, the artefact voltages produced on the reference layer leads by time-varying field gradients, cardiac pulsation and subject movement are similar to those induced in the scalp leads, but neuronal signals are not detected in the reference layer. Taking the difference of the voltages in the reference and scalp channels will therefore reduce the artefacts, without affecting sensitivity to neuronal signals. Here, we test this approach by using a simple experimental realisation of the reference layer to investigate the artefacts induced on the leads attached to the reference layer and scalp and to evaluate the degree of artefact attenuation that can be achieved via reference layer artefact subtraction (RLAS). Through a series of experiments on phantoms and human subjects, we show that RLAS significantly reduces the gradient (GA), pulse (PA) and motion (MA) artefacts, while allowing accurate recording of neuronal signals. The results indicate that RLAS generally outperforms AAS when motion is present in the removal of the GA and PA, while the combination of AAS and RLAS always produces higher artefact attenuation than AAS. Additionally, we demonstrate that RLAS greatly attenuates the unpredictable and highly variable MAs that are very hard to remove using post-processing methods.

AB - Large artefacts compromise EEG data quality during simultaneous fMRI. These artefact voltages pose heavy demands on the bandwidth and dynamic range of EEG amplifiers and mean that even small fractional variations in the artefact voltages give rise to significant residual artefacts after average artefact subtraction. Any intrinsic reduction in the magnitude of the artefacts would be highly advantageous, allowing data with a higher bandwidth to be acquired without amplifier saturation, as well as reducing the residual artefacts that can easily swamp signals from brain activity measured using current methods. Since these problems currently limit the utility of simultaneous EEG-fMRI, new approaches for reducing the magnitude and variability of the artefacts are required. One such approach is the use of an EEG cap that incorporates electrodes embedded in a reference layer that has similar conductivity to tissue and is electrically isolated from the scalp. With this arrangement, the artefact voltages produced on the reference layer leads by time-varying field gradients, cardiac pulsation and subject movement are similar to those induced in the scalp leads, but neuronal signals are not detected in the reference layer. Taking the difference of the voltages in the reference and scalp channels will therefore reduce the artefacts, without affecting sensitivity to neuronal signals. Here, we test this approach by using a simple experimental realisation of the reference layer to investigate the artefacts induced on the leads attached to the reference layer and scalp and to evaluate the degree of artefact attenuation that can be achieved via reference layer artefact subtraction (RLAS). Through a series of experiments on phantoms and human subjects, we show that RLAS significantly reduces the gradient (GA), pulse (PA) and motion (MA) artefacts, while allowing accurate recording of neuronal signals. The results indicate that RLAS generally outperforms AAS when motion is present in the removal of the GA and PA, while the combination of AAS and RLAS always produces higher artefact attenuation than AAS. Additionally, we demonstrate that RLAS greatly attenuates the unpredictable and highly variable MAs that are very hard to remove using post-processing methods.

KW - Artefact correction

KW - Artefact removal

KW - Gradient artefact

KW - Motion artefact

KW - Pulse artefact

KW - Simultaneous EEG-fMRI

UR - http://www.scopus.com/inward/record.url?scp=84884228897&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2013.08.039

DO - 10.1016/j.neuroimage.2013.08.039

M3 - Article

C2 - 23994127

AN - SCOPUS:84884228897

VL - 84

SP - 307

EP - 319

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

ER -