EPES: Seizure Propagation Analysis in an Immersive Environment
Bibliographic record
Abstract
Planning for epilepsy surgery requires precise localization of the seizure onset zone. This permits physicians to make accurate estimates about the postoperative chances of seizure freedom and the attendant risk. Patients with complex epilepsies may require intracranial electroencephalography (iEEG) to best lateralize and localize the seizure onset zone. Magnetic resonance imaging (MRI) data of implanted intracranial electrodes in a brain is used to confirm correct placement of the intracranial electrodes and to accurately map seizure onset and spread through the brain. However, the relative lack of tools co-registering iEEG with MRI data renders this a time-consuming investigation since for epilepsy specialists who have to manually map this information in 2-dimensional space. Our immersive analytics tool, EPES (Epilepsy Pre-Evaluation Space), provides an application to analyze iEEG data and its fusion with the corresponding intracranial electrodes' recordings of the brain activity. EPES highlights where a seizure is occurring and how it propagates through the virtual brain generated from the patient MRI data.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".