Successful reproduction of a large EEG study across software packages - Inserm - Institut national de la santé et de la recherche médicale Access content directly
Journal Articles Neuroimage: Reports Year : 2023

Successful reproduction of a large EEG study across software packages

Abstract

As an active field of research and with the development of state-of-the-art algorithms to analyze EEG datasets, the parametrization of Electroencephalography (EEG) analysis workflows has become increasingly flexible and complex, with a great variety of methodological options and tools to be selected at each step. This high analytical flexibility can be problematic as it can yield to variability in research outcomes. Therefore, growing attention has been recently paid to understand the potential impact of different methodological decisions on the reproducibility of results. In this paper, we aim to examine how sensitive the results of EEG analyses are to variations in preprocessing with different software tools. We reanalyzed the shared EEG data (N=500) from (Williams et al. 2021) using three of the most commonly used EEG software tools: EEGLAB, Brainstorm and FieldTrip. After reproducing the same original preprocessing workflow in each software, the resulting evoked-related potentials (ERPs) were qualitatively and quantitatively compared in order to examine the degree of consistency/discrepancy between softwares. Our findings show a good degree of convergence in terms of the general profile of ERP waveforms, peak latencies and effect size estimates related to specific signal features. However, considerable variability was also observed in the magnitude of the absolute voltage observed with each software package as reflected by the similarity values and observed statistical differences at particular channels and time instants. In conclusion, we believe that this study provides valuable clues to better understand the impact of the software tool on the analysis of EEG results.

Domains

Neuroscience
Fichier principal
Vignette du fichier
Kabbara_1-s2.0-S2666956023000144-main.pdf (5.62 Mo) Télécharger le fichier
Origin : Publisher files allowed on an open archive

Dates and versions

inserm-03747289 , version 1 (31-05-2023)

Licence

Attribution

Identifiers

Cite

Aya Kabbara, Nina Forde, Camille Maumet, Mahmoud Hassan. Successful reproduction of a large EEG study across software packages. Neuroimage: Reports, In press, 3 (2), pp.100169. ⟨10.1016/j.ynirp.2023.100169⟩. ⟨inserm-03747289⟩
121 View
100 Download

Altmetric

Share

Gmail Facebook X LinkedIn More