A combination of confirmation bias, hindsight bias, and pressure to publish may prompt the (unconscious) exploration of various methodological options and reporting only the ones that lead to a (statistically) significant outcome. This undisclosed analytic flexibility is particularly relevant in EEG research, where a myriad of preprocessing and analysis pipelines can be used to extract information from complex multidimensional data. One solution to limit confirmation and hindsight bias by disclosing analytic choices is preregistration: researchers write a time-stamped, publicly accessible research plan with hypotheses, data collection plan, and the intended preprocessing and statistical analyses before the start of a research project. In this manuscript, we present an overview of the problems associated with undisclosed analytic flexibility, discuss why and how EEG researchers would benefit from adopting preregistration, provide guidelines and examples on how to preregister data preprocessing and analysis steps in typical ERP studies, and conclude by discussing possibilities and limitations of this open science practice.

确认偏差、后见之明偏差和出版压力的结合可能会促使人们（无意识地）探索各种方法选择，并只报告那些导致（统计上）显着结果的方法。这种未公开的分析灵活性在 EEG 研究中尤为重要，其中无数的预处理和分析管道可用于从复杂的多维数据中提取信息。通过披露分析选择来限制确认和事后偏见的一种解决方案是预注册：研究人员在研究项目开始前编写一个带有时间标记的、可公开访问的研究计划，其中包含假设、数据收集计划以及预期的预处理和统计分析。在这篇手稿中，我们概述了与未公开的分析灵活性相关的问题，讨论了脑电图研究人员为什么以及如何从采用预注册中受益，提供有关如何在典型 ERP 研究中预注册数据预处理和分析步骤的指南和示例，并得出结论讨论这种开放科学实践的可能性和局限性。