#EEGManyLabs: investigating the replicability of influential EEG experiments

Research output: Contribution to journalArticlepeer-review


  • Yuri G. Pavlov
  • Nika Adamian
  • Stefan Appelhoff
  • Mahnaz Arvaneh
  • Christopher S.Y. Benwell
  • Christian Beste
  • Amy R. Bland
  • Daniel E. Bradford
  • Florian Bublatzky
  • Niko A. Busch
  • Peter E. Clayson
  • Artur Czeszumski
  • Anna Dreber
  • Guillaume Dumas
  • Benedikt Ehinger
  • Giorgio Ganis
  • Xun He
  • José A. Hinojosa
  • Christoph Huber-Huber
  • Michael Inzlicht
  • Bradley N. Jack
  • Magnus Johannesson
  • Rhiannon Jones
  • Evgenii Kalenkovich
  • Laura Kaltwasser
  • Hamid Karimi-Rouzbahani
  • Andreas Keil
  • Peter König
  • Layla Kouara
  • Louisa Kulke
  • Cecile D. Ladouceur
  • Nicolas Langer
  • Heinrich R. Liesefeld
  • David Luque
  • Annmarie MacNamara
  • Liad Mudrik
  • Muthuraman Muthuraman
  • Lauren B. Neal
  • Gustav Nilsonne
  • Guiomar Niso
  • Sebastian Ocklenburg
  • Robert Oostenveld
  • Cyril R. Pernet
  • Gilles Pourtois
  • Manuela Ruzzoli
  • Sarah M. Sass
  • Alexandre Schaefer
  • Magdalena Senderecka
  • Joel S. Snyder
  • Christian K. Tamnes
  • Emmanuelle Tognoli
  • Marieke K. van Vugt
  • Edelyn Verona
  • Robin Vloeberghs
  • Dominik Welke
  • Jan R. Wessel
  • Ilya Zakharov
  • Faisal Mushtaq

Colleges, School and Institutes

External organisations

  • Universitat Tubingen
  • Ural Federal University
  • University of Aberdeen
  • Max Planck Institute for Human Development
  • University of Sheffield
  • University of Dundee
  • Technische Universität Dresden
  • Manchester Metropolitan University
  • University of Miami
  • Ruprecht-Karls-Universität Heidelberg
  • Muenster University
  • University of South Florida
  • Universität Osnabrück
  • Stockholm School of Economics
  • University of Innsbruck
  • Université de Montréal
  • CHU Sainte-Justine Research Center
  • University of Stuttgart
  • University of Plymouth
  • Bournemouth University
  • Universidad Complutense de Madrid
  • Universidad Nebrija
  • Radboud Universiteit
  • University of Toronto
  • Australian National University
  • University of Winchester
  • National Research University Higher School of Economics
  • Humboldt-Universitat zu Berlin
  • University of Cambridge
  • Macquarie University
  • University of Florida
  • University Hospital Eppendorf
  • University of Leeds
  • Friedrich Alexander Universität Erlangen-Nürnberg
  • University of Pittsburgh
  • University of Zurich
  • Neuroscience Center Zurich
  • University of Bremen
  • University of Munich
  • Universidad Autónoma de Madrid
  • Universidad de Málaga
  • University of Texas
  • Tel Aviv University
  • Johannes Gutenberg University
  • Karolinska Institutet
  • Stockholm University
  • Indiana University
  • Universidad Politécnica de Madrid
  • Universitat Bochum
  • University of Edinburgh
  • Ghent University
  • University of Glasgow
  • The University of Texas at Tyler
  • Monash University (Malaysia Campus)
  • Jagiellonian University
  • The University of Nevada
  • University of Oslo
  • Florida Atlantic University
  • University of Groningen
  • Catholic University of Leuven
  • Max-Planck-Institute for Empirical Aesthetics
  • University of Iowa
  • Russian Academy of Education


There is growing awareness across the neuroscience community that the replicability of findings about the relationship between brain activity and cognitive phenomena can be improved by conducting studies with high statistical power that adhere to well-defined and standardised analysis pipelines. Inspired by recent efforts from the psychological sciences, and with the desire to examine some of the foundational findings using electroencephalography (EEG), we have launched #EEGManyLabs, a large-scale international collaborative replication effort. Since its discovery in the early 20th century, EEG has had a profound influence on our understanding of human cognition, but there is limited evidence on the replicability of some of the most highly cited discoveries. After a systematic search and selection process, we have identified 27 of the most influential and continually cited studies in the field. We plan to directly test the replicability of key findings from 20 of these studies in teams of at least three independent laboratories. The design and protocol of each replication effort will be submitted as a Registered Report and peer-reviewed prior to data collection. Prediction markets, open to all EEG researchers, will be used as a forecasting tool to examine which findings the community expects to replicate. This project will update our confidence in some of the most influential EEG findings and generate a large open access database that can be used to inform future research practices. Finally, through this international effort, we hope to create a cultural shift towards inclusive, high-powered multi-laboratory collaborations.

Bibliographic note

Corrected Proof available online 2/04/2021. Final Version of Record not yet available as of 23/09/2021.


Original languageEnglish
Publication statusAccepted/In press - 9 Mar 2021


  • Cognitive neuroscience, EEG, ERP, Many labs, Open science, Replication