Automagic: Standardized preprocessing of big EEG data

Electroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG recordings but mainly on account of methodological issues. In many cases, particularly in clinical, pediatric and aging populations, the EEG ha...

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Vydané v:	NeuroImage (Orlando, Fla.) Ročník 200; s. 460 - 473
Hlavní autori:	Pedroni, Andreas, Bahreini, Amirreza, Langer, Nicolas
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Inc 15.10.2019 Elsevier Limited
Predmet:	Aging Algorithms Alzheimer's disease Autism Automation Batch processing Between-subjects design BIDS Biomarkers Brain research Cerebral Cortex - physiology Computer programs Contamination EEG Electroencephalography Electroencephalography - methods Electroencephalography - standards Functional Neuroimaging - methods Functional Neuroimaging - standards Humans Initiatives Mental disorders Neuroimaging NMR Noise Nuclear magnetic resonance Physiology Preprocessing Quality Control Signal Processing, Computer-Assisted Software Artifacts BIDS Preprocessing EEG Quality control
ISSN:	1053-8119, 1095-9572, 1095-9572
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Abstract	Electroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG recordings but mainly on account of methodological issues. In many cases, particularly in clinical, pediatric and aging populations, the EEG has a high degree of artifact contamination and the quality of EEG recordings often substantially differs between subjects. Although there exists a variety of standardized preprocessing methods to clean EEG from artifacts, currently there is no method to objectively quantify the quality of preprocessed EEG. This makes the commonly accepted procedure of excluding subjects from analyses due to exceeding contamination of artifacts highly subjective. As a consequence, P-hacking is fostered, the replicability of results is decreased, and it is difficult to pool data from different study sites. In addition, in large-scale studies, data are collected over years or even decades, requiring software that controls and manages the preprocessing of ongoing and dynamically growing studies. To address these challenges, we developed Automagic, an open-source MATLAB toolbox that acts as a wrapper to run currently available preprocessing methods and offers objective standardized quality assessment for growing studies. The software is compatible with the Brain Imaging Data Structure (BIDS) standard and hence facilitates data sharing. In the present paper we outline the functionality of Automagic and examine the effect of applying combinations of methods on a sample of resting and task-based EEG data. This examination suggests that applying a pipeline of algorithms to detect artifactual channels in combination with Multiple Artifact Rejection Algorithm (MARA), an independent component analysis (ICA)-based artifact correction method, is sufficient to reduce a large extent of artifacts. •Automagic is a new EEG preprocessing toolbox for large and growing studies.•Automagic is open source and wraps a selection of available preprocessing tools.•Automagic is BIDS compatible and offers standardized quality metrics.•Bad channel detection and MARA sufficiently reduces artifacts from resting EEG.
AbstractList	Electroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG recordings but mainly on account of methodological issues. In many cases, particularly in clinical, pediatric and aging populations, the EEG has a high degree of artifact contamination and the quality of EEG recordings often substantially differs between subjects. Although there exists a variety of standardized preprocessing methods to clean EEG from artifacts, currently there is no method to objectively quantify the quality of preprocessed EEG. This makes the commonly accepted procedure of excluding subjects from analyses due to exceeding contamination of artifacts highly subjective. As a consequence, P-hacking is fostered, the replicability of results is decreased, and it is difficult to pool data from different study sites. In addition, in large-scale studies, data are collected over years or even decades, requiring software that controls and manages the preprocessing of ongoing and dynamically growing studies. To address these challenges, we developed Automagic, an open-source MATLAB toolbox that acts as a wrapper to run currently available preprocessing methods and offers objective standardized quality assessment for growing studies. The software is compatible with the Brain Imaging Data Structure (BIDS) standard and hence facilitates data sharing. In the present paper we outline the functionality of Automagic and examine the effect of applying combinations of methods on a sample of resting and task-based EEG data. This examination suggests that applying a pipeline of algorithms to detect artifactual channels in combination with Multiple Artifact Rejection Algorithm (MARA), an independent component analysis (ICA)-based artifact correction method, is sufficient to reduce a large extent of artifacts. Electroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG recordings but mainly on account of methodological issues. In many cases, particularly in clinical, pediatric and aging populations, the EEG has a high degree of artifact contamination and the quality of EEG recordings often substantially differs between subjects. Although there exists a variety of standardized preprocessing methods to clean EEG from artifacts, currently there is no method to objectively quantify the quality of preprocessed EEG. This makes the commonly accepted procedure of excluding subjects from analyses due to exceeding contamination of artifacts highly subjective. As a consequence, P-hacking is fostered, the replicability of results is decreased, and it is difficult to pool data from different study sites. In addition, in large-scale studies, data are collected over years or even decades, requiring software that controls and manages the preprocessing of ongoing and dynamically growing studies. To address these challenges, we developed Automagic, an open-source MATLAB toolbox that acts as a wrapper to run currently available preprocessing methods and offers objective standardized quality assessment for growing studies. The software is compatible with the Brain Imaging Data Structure (BIDS) standard and hence facilitates data sharing. In the present paper we outline the functionality of Automagic and examine the effect of applying combinations of methods on a sample of resting and task-based EEG data. This examination suggests that applying a pipeline of algorithms to detect artifactual channels in combination with Multiple Artifact Rejection Algorithm (MARA), an independent component analysis (ICA)-based artifact correction method, is sufficient to reduce a large extent of artifacts. •Automagic is a new EEG preprocessing toolbox for large and growing studies.•Automagic is open source and wraps a selection of available preprocessing tools.•Automagic is BIDS compatible and offers standardized quality metrics.•Bad channel detection and MARA sufficiently reduces artifacts from resting EEG. Electroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG recordings but mainly on account of methodological issues. In many cases, particularly in clinical, pediatric and aging populations, the EEG has a high degree of artifact contamination and the quality of EEG recordings often substantially differs between subjects. Although there exists a variety of standardized preprocessing methods to clean EEG from artifacts, currently there is no method to objectively quantify the quality of preprocessed EEG. This makes the commonly accepted procedure of excluding subjects from analyses due to exceeding contamination of artifacts highly subjective. As a consequence, P-hacking is fostered, the replicability of results is decreased, and it is difficult to pool data from different study sites. In addition, in large-scale studies, data are collected over years or even decades, requiring software that controls and manages the preprocessing of ongoing and dynamically growing studies. To address these challenges, we developed Automagic, an open-source MATLAB toolbox that acts as a wrapper to run currently available preprocessing methods and offers objective standardized quality assessment for growing studies. The software is compatible with the Brain Imaging Data Structure (BIDS) standard and hence facilitates data sharing. In the present paper we outline the functionality of Automagic and examine the effect of applying combinations of methods on a sample of resting and task-based EEG data. This examination suggests that applying a pipeline of algorithms to detect artifactual channels in combination with Multiple Artifact Rejection Algorithm (MARA), an independent component analysis (ICA)-based artifact correction method, is sufficient to reduce a large extent of artifacts.Electroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG recordings but mainly on account of methodological issues. In many cases, particularly in clinical, pediatric and aging populations, the EEG has a high degree of artifact contamination and the quality of EEG recordings often substantially differs between subjects. Although there exists a variety of standardized preprocessing methods to clean EEG from artifacts, currently there is no method to objectively quantify the quality of preprocessed EEG. This makes the commonly accepted procedure of excluding subjects from analyses due to exceeding contamination of artifacts highly subjective. As a consequence, P-hacking is fostered, the replicability of results is decreased, and it is difficult to pool data from different study sites. In addition, in large-scale studies, data are collected over years or even decades, requiring software that controls and manages the preprocessing of ongoing and dynamically growing studies. To address these challenges, we developed Automagic, an open-source MATLAB toolbox that acts as a wrapper to run currently available preprocessing methods and offers objective standardized quality assessment for growing studies. The software is compatible with the Brain Imaging Data Structure (BIDS) standard and hence facilitates data sharing. In the present paper we outline the functionality of Automagic and examine the effect of applying combinations of methods on a sample of resting and task-based EEG data. This examination suggests that applying a pipeline of algorithms to detect artifactual channels in combination with Multiple Artifact Rejection Algorithm (MARA), an independent component analysis (ICA)-based artifact correction method, is sufficient to reduce a large extent of artifacts.
Author	Bahreini, Amirreza Langer, Nicolas Pedroni, Andreas
Author_xml	– sequence: 1 givenname: Andreas surname: Pedroni fullname: Pedroni, Andreas email: anpedroni@gmail.com organization: Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland – sequence: 2 givenname: Amirreza surname: Bahreini fullname: Bahreini, Amirreza organization: Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland – sequence: 3 givenname: Nicolas surname: Langer fullname: Langer, Nicolas organization: Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31233907$$D View this record in MEDLINE/PubMed
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ContentType	Journal Article
Copyright	2019 Elsevier Inc. Copyright © 2019 Elsevier Inc. All rights reserved. 2019. Elsevier Inc.
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Snippet	Electroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG...
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SubjectTerms	Aging Algorithms Alzheimer's disease Autism Automation Batch processing Between-subjects design BIDS Biomarkers Brain research Cerebral Cortex - physiology Computer programs Contamination EEG Electroencephalography Electroencephalography - methods Electroencephalography - standards Functional Neuroimaging - methods Functional Neuroimaging - standards Humans Initiatives Mental disorders Neuroimaging NMR Noise Nuclear magnetic resonance Physiology Preprocessing Quality Control Signal Processing, Computer-Assisted Software
Title	Automagic: Standardized preprocessing of big EEG data
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