From BIDS-Formatted EEG Data to Sensor-Space Group Results: A Fully Reproducible Workflow With EEGLAB and LIMO EEG.
EEGLAB toolbox
LIMO EEG
brain imaging data structure
linear models
preprocessing algorithm
reproducibility and tools
Journal
Frontiers in neuroscience
ISSN: 1662-4548
Titre abrégé: Front Neurosci
Pays: Switzerland
ID NLM: 101478481
Informations de publication
Date de publication:
2020
2020
Historique:
received:
25
09
2020
accepted:
11
12
2020
entrez:
1
2
2021
pubmed:
2
2
2021
medline:
2
2
2021
Statut:
epublish
Résumé
Reproducibility is a cornerstone of scientific communication without which one cannot build upon each other's work. Because modern human brain imaging relies on many integrated steps with a variety of possible algorithms, it has, however, become impossible to report every detail of a data processing workflow. In response to this analytical complexity, community recommendations are to share data analysis pipelines (scripts that implement workflows). Here we show that this can easily be done using EEGLAB and tools built around it. BIDS tools allow importing all the necessary information and create a study from electroencephalography (EEG)-Brain Imaging Data Structure compliant data. From there preprocessing can be carried out in only a few steps using EEGLAB and statistical analyses performed using the LIMO EEG plug-in. Using Wakeman and Henson (2015) face dataset, we illustrate how to prepare data and build different statistical models, a standard factorial design (faces
Identifiants
pubmed: 33519362
doi: 10.3389/fnins.2020.610388
pmc: PMC7845738
doi:
Types de publication
Journal Article
Langues
eng
Pagination
610388Informations de copyright
Copyright © 2021 Pernet, Martinez-Cancino, Truong, Makeig and Delorme.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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