Hierarchical Event Descriptor library schema for EEG data annotation
Bibliographic record
Abstract
Standardizing terminology to annotate electrophysiological events can improve both computational research and clinical care. Enriching data with standard terms facilitates data exploration, from case studies to mega-analyses. The machine readability of such electrophysiological event annotations is essential for performing automated analyses. The Hierarchical Event Descriptor (HED) framework provides a standard for describing events in neuroscience experiments but does not yet include terms for electrophysiological data features. The Standardized Computer-based Organized Reporting of EEG (SCORE) defines terms for EEG features but is not yet openly available in machine-readable format. This study therefore developed a HED library schema for SCORE: the HED-SCORE library schema. This library schema makes the SCORE terms machine-readable and searchable and extents the standard HED schema with a controlled hierarchical vocabulary to annotate electrophysiological events. We demonstrate that the HED-SCORE library schema can be used to annotate events in EEG data stored in the Brain Imaging Data Structure (BIDS). Clinicians and researchers worldwide can use the HED-SCORE library schema to annotate and compute on human electrophysiological data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| 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".