Electroencephalography (EEG) is widely used in psychophysiological research. However, the analytical flexibility of EEG has challenged the robustness of EEG findings. Since there are so many various ways to process and analyze EEG data, analysis pipelines vary greatly between studies. It is currently unclear to what extent alternate, plausible pipelines produce various findings and assumptions. The EEGManyPipelines project is inspired by other recent projects involving many independent analysis teams to investigate a) how different analysts approach a given data set and b) how analysis approaches affect the obtained results. EEGManyPipelines extends this novel initiative to EEG research. 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 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. Thereby, EEGManyPipelines will help assessing the robustness of EEG findings across alternative analyses, identifying (sub)optimal analysis pipelines, and informing guidelines for reporting EEG analyses in publications. Thus, we expect that EEGManyPipelines will help improving the credibility of EEG research and the quality of analyses, and will inspire new standards for conducting and reporting EEG studies. Given the widespread use of EEG in human cognitive neuroscience and psychology, this project represents a timely and crucial endeavour that will benefit the cognitive neuroscience community at large.