Evidence map›Paper›PMID 37767154›Full record

ArticleMethodsX2023

Broadly applicable methods for the detection of artefacts in electroencephalography acquired simultaneously with hemodynamic recordings.

Rachel Nuttall, Aya El Mir, Cilia Jäger, Svenja Letz, Afra Wohlschläger, Gerhard Schneider

Abstract read
In one paragraph

Article in MethodsX, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Rachel NuttallDepartment of Anesthesiology and Intensive Care, School of Medicine, Technical University of Munich, Klinikum rechts der Isar, Munich 81675, Germany.
Aya El MirDepartment of Anesthesiology and Intensive Care, School of Medicine, Technical University of Munich, Klinikum rechts der Isar, Munich 81675, Germany.
Cilia JägerDepartment of Neuroradiology, School of Medicine, Technical University of Munich, Klinikum rechts der Isar, Munich 81675, Germany.
Svenja LetzDepartment of Anesthesiology and Intensive Care, School of Medicine, Technical University of Munich, Klinikum rechts der Isar, Munich 81675, Germany.
Afra WohlschlägerDepartment of Neuroradiology, School of Medicine, Technical University of Munich, Klinikum rechts der Isar, Munich 81675, Germany.
Gerhard SchneiderDepartment of Anesthesiology and Intensive Care, School of Medicine, Technical University of Munich, Klinikum rechts der Isar, Munich 81675, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electroencephalography (EEG) data, acquired simultaneously with magnetic resonance imaging (MRI), must be corrected for artefacts related to MR gradient switches (GS) and the cardioballistic (CB) effect. Canonical approaches require additional signal acquisition for artefact detection (e.g., MR volume onsets, ECG), without which the EEG data would be rendered uncleanable from these artefacts.•We present two broadly applicable methods for artefact detection based on peak detection combined with temporal constraints with respect to periodicity directly from the EEG data itself; no additional signals are required. We validated the performance of our methods versus the two canonical approaches for detection of GS/CB artefact, respectively, on 26 healthy human EEG-functional MRI resting-state datasets. Utilising various performance metrics, we found our methods to perform as well as - and sometimes better than - the canonical standard approaches. With as little as one EEG channel recording, our methods can be applied to detect GS/CB artefacts in EEG data acquired simultaneously with MRI in the absence of MR volume onsets and/or an ECG recording. The detected artefact onsets can then be fed into the standard artefact correction software.

Indexed as

ArtefactArtifindBallistocardiacCardioballisticECGEEG-fMRIGradientPulse

Identifiers

PMID37767154
PMCPMC10520509

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.