P.087 Methods for representing dipole distribution in high-density EEG source localization for focal epilepsy: a systematic analysis
Bibliographic record
Abstract
Background: Routine electroencephalography (EEG) provides excellent temporal resolution for evaluation of focal epilepsy, but lacks spatial resolution. High-density-EEG (HDEEG)-based source-localization significantly enhances spatial resolution, but requires greater standardization. We systematically review HDEEG systems, methods, and metrics utilized for evaluating focal epilepsy. Methods: A systematic search was conducted in PubMed using PRISMA guidelines with keywords “HDEEG” or “high-density EEG”, “source localization and “focal epilepsy”. Inclusion criteria: studies from the last 20 years, human subjects with focal epilepsy, sample size ≥ 10 and HDEEG with source localization methods clearly described. Results: 37 of 65 studies fulfilled inclusion criteria, with most reporting N<50. Most studies (14) used a 256-electrode HDEEG setup; 10 used 128-electrode configurations, and 6 used 76–83 electrodes. EEG source localization most commonly used Cartool (N=12) and Curry (N=5) softwares. Standard MRIs were used in 25 studies, and customized MRIs in 12. Metrics like clustering coefficient were reported to represent dipole distribution (10 studies); while functional connectivity analysis was reported in 7 studies. Conclusions: Variations in software choice, metrics for dipole distribution assessment, and MRI integration are evident from the current literature. Clustering methods and functional connectivity metrics are most commonly employed to represent dipole distribution, reflecting their increasing utility in understanding brain networks.
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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 | 0.026 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".