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Making ERP research more transparent: Guidelines for preregistration.

Mariella Paul1, Gisela H Govaart2, Antonio Schettino3

  • 1Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraβe 1a, 04103 Leipzig, Germany; Humboldt-Universität zu Berlin, Faculty of Philosophy, Berlin School of Mind and Brain, Luisenstraße 56, 10117 Berlin, Germany; Psychology of Language Department, University of Göttingen, Goßlerstraße 14, 37073 Göttingen, Germany.

International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology
|March 7, 2021
PubMed
Summary

Preregistration in electroencephalography (EEG) research combats bias by disclosing analytic choices upfront. This open science practice enhances transparency and reproducibility in EEG studies.

Keywords:
EEGERPOpen sciencePreregistration

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Psychology

Background:

  • Undisclosed analytic flexibility in EEG research can be influenced by biases like confirmation and hindsight bias, alongside publication pressure.
  • The complex nature of electroencephalography (EEG) data allows for numerous preprocessing and analysis pathways, increasing the potential for undisclosed analytic flexibility.
  • This flexibility can lead to reporting only statistically significant outcomes, potentially compromising research integrity.

Purpose of the Study:

  • To address the issue of undisclosed analytic flexibility in EEG research.
  • To advocate for and provide guidance on the adoption of preregistration in EEG studies.
  • To discuss the benefits, limitations, and practical implementation of preregistration for EEG data preprocessing and analysis.

Main Methods:

  • The manuscript reviews the problems stemming from undisclosed analytic flexibility in scientific research.
  • It discusses the advantages of preregistration as a method to mitigate these biases in EEG research.
  • Guidelines and examples are provided for preregistering preprocessing and analysis steps in event-related potential (ERP) studies.

Main Results:

  • Preregistration offers a structured approach to limit confirmation and hindsight bias by requiring researchers to document their analytic plans before data collection.
  • Disclosing analytic choices through preregistration enhances the transparency and reproducibility of EEG research findings.
  • The study outlines practical steps for implementing preregistration in typical ERP analyses.

Conclusions:

  • Adopting preregistration is crucial for improving the rigor and reliability of EEG research.
  • Preregistration serves as a key open science practice to ensure the integrity of scientific findings.
  • While offering significant benefits, the practical application of preregistration in EEG research also has limitations that need consideration.