The GREENBEAN checklist for reporting studies evaluating the effectiveness of EEG-based biomarkers
Bibliographic record
Abstract
Advances in digital technology, signal analysis, and data science have led to a rapid increase in papers reporting EEG-based biomarkers. However, wide heterogeneity in study design and reporting poses challenges in assessing the reliability, validity and utility of these biomarkers. In this evolving field, best practices are sometimes debated but not yet rigorously defined, and the appropriate next step is to ensure that validation-focused research manuscripts report key methodological factors that are known or suspected to influence results. To assist authors in designing and reporting validation studies of EEG biomarkers, and to help editors and regulatory bodies evaluate them, an international working group-under the auspices of the International Federation of Clinical Neurophysiology (IFCN) and in collaboration with the EQUATOR Network-developed the Guidelines for Reporting EEG/Neurophysiology Biomarker Evaluation for Application to Neurology and Neuropsychiatry (GREENBEAN). EEG biomarker validation studies are classified into four phases, similarly to therapeutic studies. Phases 1-2 are preliminary and do not constitute formal validation. Phase 3 studies provide compelling evidence of validity, while phase 4 studies assess the clinical utility and generalizability of previously validated biomarkers within real-world settings. We provide detailed definitions for each phase, along with a checklist of items to address and report. A detailed Explanation and Elaboration document is included in Supplementary Material with multiple examples of how to design and report EEG biomarker studies. We expect that more transparent reporting regarding experimental design and technical standards will not only enhance short-term biomarker validation efforts but will also enhance methodological research to make future efforts more efficient and effective.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.392 | 0.628 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.038 | 0.023 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.013 | 0.014 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.042 | 0.024 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".