Abstract

Electroencephalographic (EEG) monitoring during functional magnetic resonance imaging (fMRI) experiments is increasingly applied for studying physiological and pathological brain function. However, the quality of the fMRI data can be significantly compromised by the EEG recording due to the magnetic susceptibility of the EEG electrode assemblies and electromagnetic noise emitted by the EEG recording equipment. We therefore investigated the effect of individual components of the EEG recording equipment on the quality of echo planar images. The artifact associated with each component was measured and compared to the minimum scalp-cortex distance measured in normal controls. The image noise originating from the EEG recording equipment was identified as coherent noise and could be eliminated by appropriate shielding of the EEG equipment. It was concluded that concurrent EEG and fMRI could be performed without compromising the image quality significantly if suitable equipment is used. The methods described and the results of this study should be useful to other researchers as a framework for testing of their own equipment and for the selection of appropriate equipment for EEG recording inside a MR scanner.

Keywords

ElectroencephalographyEEG-fMRIArtifact (error)Functional magnetic resonance imagingNoise (video)Image qualityComputer scienceScannerArtificial intelligenceComputer visionPsychologyNeuroscienceImage (mathematics)

MeSH Terms

AdolescentAdultArtifactsCerebral CortexElectroencephalographyFemaleHumansImage EnhancementMagnetic Resonance ImagingMaleMiddle AgedScalp

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Publication Info

Year
2000
Type
article
Volume
10
Issue
1
Pages
10-15
Citations
147
Access
Closed

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147
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7
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123
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Cite This

Karsten Krakow, Philip J. Allen, Mark R. Symms et al. (2000). EEG recording during fMRI experiments: Image quality. Human Brain Mapping , 10 (1) , 10-15. https://doi.org/10.1002/(sici)1097-0193(200005)10:1<10::aid-hbm20>3.0.co;2-t

Identifiers

DOI
10.1002/(sici)1097-0193(200005)10:1<10::aid-hbm20>3.0.co;2-t
PMID
10843514
PMCID
PMC6872036

Data Quality

Data completeness: 86%