Electroencephalography (EEG) is widely used to investigate human cognition. However, the credibility of EEG findings has been called into question. Analysis pipelines are highly variable across studies, because of the many ways in which EEG data can be processed and analysed. The degree to which alternative, plausible pipelines yield different results and conclusions is currently unknown.

This proposal presents EEGManyPipelines, a large-scale international collaborative project addressing the robustness of EEG research by asking many independent teams to analyse the same data with an analysis pipeline they deem sensible and representative of their own research. Analysts will report their results and a detailed description of the analysis pipeline, allowing us to analyze the diversity of analysis pipelines and their effects on results. Thus, this project will help assess the robustness of EEG findings across alternative analyses, identifying (sub)optimal analysis pipelines, and informing guidelines for reporting EEG analyses in publications.

We expect to facilitate a cultural shift away from small-scale single laboratory experiments towards high-powered, community driven collaborations. This project will help improve the credibility of EEG findings and the quality of analyses, and will inspire new standards for conducting and reporting EEG studies, thereby supporting the foundation of future EEG research.