The effect of tissue anisotropy on the EEG inverse problem
Bibliographic record
Abstract
Electroencephalogram (EEG) source analysis is typically done using head models with isotropic tissue conductivities. This ignores the fact that some tissues are known to be highly anisotropic. In this study, we investigate the effect that tissue anisotropy has on the EEG inverse problem. EEG electrode voltages are simulated using the forward problem solution for an anisotropic head model, while the inverse problem is solved for an isotropic head model. The error associated with neglecting anisotropy is quantified with the source localization error. A realistic head model is generated from magnetic resonance images and anisotropic conductivity values are estimated from diffusion tensor images. All calculations are done with the finite volume method on a cubic grid with 1 mm resolution. We determine that neglecting to account for anisotropy can cause considerable source localization errors, indicating that the anisotropic conductivities should not be ignored in EEG source analysis.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".