Source Localization of Normal Variants Seen on EEG
Bibliographic record
Abstract
PURPOSE: The EEG is an essential neurological diagnostic tool. EEG abnormalities can guide diagnosis and management of epilepsy. There are also distinctive EEG waveforms that are seen in healthy individuals. It is critical not to misinterpret these as abnormal. To emphasize the importance of these waveforms, we analyzed different normal variants via the source localization technology. METHODS: This is a retrospective analysis of EEGs performed at the Duke University Hospital between June 2014 and Dec 2019. We selected samples of vertex waves, Mu, lambda, POSTS, wickets, and sleep spindles for analysis. EEG were imported to Curry 8 (Compumedics) to calculate the dipole and current density. The averaged head model from the Montreal Neurological Institute database was used for reconstruction. RESULTS: Thirty-four patient EEG samples were selected including five vertex, six Mu, four wicket, seven lambda, five POSTS, and seven spindles. Results from source localization showed that vertex waves are localized in the frontocentral area, whereas spindles in the deep midline central region. Mu were identified in the ipsilateral somatosensory cortex. Lambda and POSTS, on the other hand, had maximum results over the bilateral occipital region and wickets in the ipsilateral temporal lobe. CONCLUSIONS: Our results confirm and expand previous hypotheses. This allows us to speculate on the origin of these normal EEG variants. Although this study is limited by small sample size, lack of high-density EEG, and patient-specific MRI, our analysis provides an easily replicable three-dimensional visualization of these waveforms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".