o:26100 Towards ARTEM-IS: design guidelines for evidence-based EEG methodology reporting tools en Аbstract: As the number of EEG papers increases, so too do the number of guidelines for how to report what has been done. However, current guidelines and checklists appear to have limited adoption, as systematic reviews have shown the journal article format is highly prone to errors, ambiguities and omissions of methodological details. This is a problem for transparency in the scientific record, along with reproducibility and metascience. Following lessons learned in the high complexity fields of aviation and surgery, we conclude that new tools are needed to overcome the limitations of written methodology descriptions, and that these tools should be developed through community consultation to ensure that they have the most utility for EEG stakeholders. As a first step in tool development, we present the ARTEM-IS Statement describing what action will be needed to create an Agreed Reporting Template for Electroencephalography Methodology - International Standard (ARTEM-IS), along with ARTEM-IS Design Guidelines for developing tools that use an evidence-based approach to error reduction. We first launched the statement at the LiveMEEG conference in 2020 along with a draft of an ARTEM-IS template for public consultation. Members of the EEG community are invited to join this collective effort to create evidence- based tools that will help make the process of reporting methodology intuitive to complete and foolproof by design https://www.sciencedirect.com/science/article/pii/S1053811921009939?via%3Dihub Keywords: EEG Neuroscience Methodology Human error International standards ARTEM-IS 1552099 10.1016/j.neuroimage.2021.118721 1552101 1053-8119 2022-06-23T08:43:21.740Z 44 yes 46 Анђела Шошкић Универзитет у Београду Учитељски факултет 0000-0003-0216-989X application/pdf 754977 https://phaidrabg.bg.ac.rs/o:26100 no yes 18 70 92000001 11A42 NeuroImage 118721 245 Elsevier Academic Press 2021 https://www.sciencedirect.com/journal/neuroimage