<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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".