Scalp electrode placement accuracy for the canine electroencephalography array
Bibliographic record
Abstract
Introduction: Despite the common occurrence of idiopathic epilepsy amongst neurological conditions in dogs, electroencephalography (EEG), the gold standard for seizure detection, is relatively neglected. The use of EEG in veterinary medicine is rudimentary compared to that in human medicine, particularly with respect to the quantification of EEG electrode placement error, i.e., the accuracy of electrode placement relative to the diverse canine cortical topography. Methods: = 5) dogs from breeds with archetypal skull conformation. For the Mesocephalic cohort, a stereotactic brain atlas was incorporated into the brain models to quantify electrode placement error via a universal coordinate system. As this was not possible for the Brachycephalic and Dolichocephalic cohort, instead electrode placement was described in relation to cortical landmarks. Results: Gaps in cortical coverage between cohorts were identified, such as poor coverage of the olfactory and frontal regions in the brachycephalic cohort and the parietal region in the Mesocephalic and Dolichocephalic cohorts. Quantitative analysis of electrode placement in the Mesocephalic cohort showed the minimum variance of electrode localization for the x coordinate of the F8 electrode (0.8 mm) and the greatest variance for the y coordinate of the Cz electrode (35.2 mm). Discussion: This is the first study to highlight the knowledge gaps regarding the accuracy of canine EEG electrode localization, differences in the array coverage across the diverse canine skull conformations, and the urgent need for a stereotactic brain atlas for specific canine skull conformations.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".