EEGManyPipelines

News


Project description

We are delighted to announce the official launch of the EEGManyPipelines project! This project is inspired by other recent projects involving many independent analysis teams to investigate how different analysts approach a given data set and how analysis approaches affect the obtained results (e.g., Silberzahn et al., 2015; Botvinik-Nezer et al., 2020). The aim of this project is to extend this novel initiative to EEG research. We believe this to be particularly important in the case of EEG data, as compared to other neuroimaging research, analysis pipelines are less standardized (e.g. see Cohen, 2017) and have more degrees of freedom. EEG is the most widespread tool in human neuroscience research with significant impact on research in all fields of psychology and cognitive neuroscience, which, we believe, makes the EEGManyPipelines project a timely and crucial endeavor that we hope will benefit a large part of the cognitive neuroscience community.

Participants in this project will get access to an EEG dataset and are invited to analyze the data with an analysis pipeline they deem sensible and representative of their own research. Participants will then report their results and a detailed description of the analysis pipeline back to us. We will use these reports to map the diversity of analysis pipelines and the effect of pipeline parameters on obtained results.

See all details on the people involved in the project: People Involved in EEGManyPipelines

See the full list of publications on the Publications page.

For questions or comments, please write email to eegmanypipelines@gmail.com.

Funding

The EEGManyPipelines project is supported by grants from the German Research Foundation (DFG) to Niko Busch and by the DFG priority program "META-REP: A Meta-scientific Programme to Analyse and Optimise Replicability in the Behavioural, Social, and Cognitive Sciences" and supported by a grant from Riksbankens Jubileumsfond to Gustav Nilsonne, Niko Busch, and Mikkel C. Vinding.

References

[1] Botvinik-Nezer R, Holzmeister F, ..., Schonerg T (2020): Variability in the analysis of a single neuroimaging dataset by many teams. Nature 582:84–88.
[2] Cohen, MX (2017): Rigor and replication in time-frequency analyses of cognitive electrophysiology data. International Journal of Psychophysiology 111:80–87.
[3] Silberzahn R, Uhlmann EL, ..., Nosek BA (2018): Many analysts, one dataset: making transparent how variations in analytic choices affect results. Advances in Methods and Practices in Psychological Science 1(3):337–356.
This page was last modified on 2026-05-12.