<scp>ARTEM</scp> ‐ <scp>IS</scp> for <scp>ERP</scp> : Agreed Reporting Template for <scp>EEG</scp> Methodology—International Standard for Event‐Related Potential Experiments

The choices we make during the recording, preprocessing and analysis of event-related potentials (ERP) data can affect study outcomes. As such, it is critical that they are transparently reported to allow for reproducibility. Yet, systematic reviews of reporting practices in the field have shown that journal articles often do not meet this goal and that existing reporting guidelines have not resulted in a sufficient improvement in reporting transparency. An easier workflow for transparently documenting pipelines used in regular journal articles is needed. The ARTEM-IS (Agreed Reporting Template for EEG Methodology-International Standard) initiative is working towards addressing this issue by building dynamic, interactive web applications that support documenting information required by existing publication guidelines in the form of a standardized metadata template. Completing an ARTEM-IS form results in a human-reader-friendly PDF or DOCX and a machine-readable JSON summary of methodological information. This level of specificity surpasses conventional article methods sections, ensuring fewer omissions and ambiguities. These can be used as supplements to a publication, as a memory aid when writing a paper, or as records that allow easier metadata extraction. Here, we present the ARTEM-IS for ERP, which supports describing a typical ERP study, including most of its core methodological aspects (study description, experimental design, hardware, data acquisition, pre-processing, measurement, visualization, additional comments). We discuss the current functionalities of ARTEM-IS for ERP, its development via a grassroots collaborative initiative, and potential extensions (e.g., including complex designs or statistical analyses). In doing so, we highlight how widespread adoption of ARTEM-IS can benefit researchers, reviewers, and the broader scientific community by improving transparency, reducing reporting errors, and expediting rigorous replication efforts.