Abstract
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.
Highlights
As data analyses become more and more complex, it has been advocated that clear workflows and all of the parameters used in their implementation should be reported in order to increase reproducibility (Pernet et al, 2020)
The pipeline for the presented analysis is available at https://github.com/LIMO-EEGToolbox/limo_meeg/tree/master/resources/from_bids2stats.m and further designs presented on the LIMO MEEG GitHub website
Note that from raw data imported into EEGLAB, those tools allow just as easy to export in the BIDS compliant format
Summary
As data analyses become more and more complex, it has been advocated that clear workflows and all of the parameters used in their implementation should be reported in order to increase reproducibility (Pernet et al, 2020). One solution is to report in detail the workflow and share the corresponding pipelines— only having to communicate key algorithm details Such pipelines and/or tools to build pipelines have been developed in recent years (see, e.g., Bigdely-Shamlo et al, 2015; Andersen, 2018; Jas et al, 2018; Niso et al, 2019; Meunier et al, 2020) and here we describe tools developed around EEGLAB (Delorme and Makeig, 2004) which allow creating a fully reproducible pipeline from raw data to group results, with an example to sensor space analysis. Reproducible EEG Using EEGLAB-LIMO the Brain Imaging Data Structure (Gorgolewski et al, 2016) and its EEG extension (Pernet et al, 2019) allow defining important EEG metadata information, such as additional event information, electrode positions, and experimental conditions This makes data aggregation from different experiments and analysis automation using standardized pipelines easier. We present a fully reproducible workflow (Figure 1) from raw data to group results using open data and we document and share the pipeline
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