Displaying EEG data for neurosurgical guidance
Bibliographic record
Abstract
Image-guided neurosurgery (IGNS) in which anatomical images generated from patient MRI or CT scans provide surgical guidance, is now routinely employed in numerous institutions. However, IGNS systems generally lack the ability to display functional data, a significant shortcoming for many types of procedure. We have enhanced our IGNS system allowing the surgeon to display and interact with patient electroencephalography (EEG) data in the operating room. The surgeon can: determine 3D electrode locations; display electrode locations with respect to the underlying 3D patient anatomy obtained from MRI; visualize the EEG potential field map interpolated onto the scalp; graphically analyze the time evolution of these maps; and view the location of equivalent sources within the patient cerebral structures. Display of EEG information is clinically significant in cases involving the surgical treatment of epilepsy, where EEG data plays an important role in characterizing and localizing epileptic foci, both preoperatively and during the operation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".