Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162129
Title: Towards ARTEM-IS: design guidelines for evidence-based EEG methodology reporting tools
Authors: Styles, Suzy J.
Ković, Vanja
Ke, Han
Šoškić, Anđela
Keywords: Social sciences::Psychology
Issue Date: 2021
Source: Styles, S. J., Ković, V., Ke, H. & Šoškić, A. (2021). Towards ARTEM-IS: design guidelines for evidence-based EEG methodology reporting tools. NeuroImage, 245, 118721-. https://dx.doi.org/10.1016/j.neuroimage.2021.118721
Project: NRF2016-SOL002-011 
JHU IO 90071537 
M4081215
Journal: NeuroImage
Abstract: 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.
URI: https://hdl.handle.net/10356/162129
ISSN: 1053-8119
DOI: 10.1016/j.neuroimage.2021.118721
Rights: © 2021 Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SSS Journal Articles

Files in This Item:
File Description SizeFormat 
1-s2.0-S1053811921009939-main.pdf737.28 kBAdobe PDFView/Open

SCOPUSTM   
Citations 50

4
Updated on Nov 30, 2022

Web of ScienceTM
Citations 50

4
Updated on Nov 28, 2022

Page view(s)

7
Updated on Dec 3, 2022

Download(s)

2
Updated on Dec 3, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.