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From BIDS-Formatted EEG Data to Sensor-Space Group Results: A Fully Reproducible Workflow With EEGLAB and LIMO EEG

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...

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Autores principales: Pernet, Cyril R., Martinez-Cancino, Ramon, Truong, Dung, Makeig, Scott, Delorme, Arnaud
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7845738/
https://www.ncbi.nlm.nih.gov/pubmed/33519362
http://dx.doi.org/10.3389/fnins.2020.610388
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author Pernet, Cyril R.
Martinez-Cancino, Ramon
Truong, Dung
Makeig, Scott
Delorme, Arnaud
author_facet Pernet, Cyril R.
Martinez-Cancino, Ramon
Truong, Dung
Makeig, Scott
Delorme, Arnaud
author_sort Pernet, Cyril R.
collection PubMed
description 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 (∗) repetition), and a more modern trial-based regression approach for the stimulus repetition effect, all in a few reproducible command lines.
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spelling pubmed-78457382021-01-30 From BIDS-Formatted EEG Data to Sensor-Space Group Results: A Fully Reproducible Workflow With EEGLAB and LIMO EEG Pernet, Cyril R. Martinez-Cancino, Ramon Truong, Dung Makeig, Scott Delorme, Arnaud Front Neurosci Neuroscience 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 (∗) repetition), and a more modern trial-based regression approach for the stimulus repetition effect, all in a few reproducible command lines. Frontiers Media S.A. 2021-01-11 /pmc/articles/PMC7845738/ /pubmed/33519362 http://dx.doi.org/10.3389/fnins.2020.610388 Text en Copyright © 2021 Pernet, Martinez-Cancino, Truong, Makeig and Delorme. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Pernet, Cyril R.
Martinez-Cancino, Ramon
Truong, Dung
Makeig, Scott
Delorme, Arnaud
From BIDS-Formatted EEG Data to Sensor-Space Group Results: A Fully Reproducible Workflow With EEGLAB and LIMO EEG
title From BIDS-Formatted EEG Data to Sensor-Space Group Results: A Fully Reproducible Workflow With EEGLAB and LIMO EEG
title_full From BIDS-Formatted EEG Data to Sensor-Space Group Results: A Fully Reproducible Workflow With EEGLAB and LIMO EEG
title_fullStr From BIDS-Formatted EEG Data to Sensor-Space Group Results: A Fully Reproducible Workflow With EEGLAB and LIMO EEG
title_full_unstemmed From BIDS-Formatted EEG Data to Sensor-Space Group Results: A Fully Reproducible Workflow With EEGLAB and LIMO EEG
title_short From BIDS-Formatted EEG Data to Sensor-Space Group Results: A Fully Reproducible Workflow With EEGLAB and LIMO EEG
title_sort from bids-formatted eeg data to sensor-space group results: a fully reproducible workflow with eeglab and limo eeg
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7845738/
https://www.ncbi.nlm.nih.gov/pubmed/33519362
http://dx.doi.org/10.3389/fnins.2020.610388
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