Simultaneous MEG and EEG source analysis
Bibliographic record
Abstract
A method is described to derive source and conductivity estimates in a simultaneous MEG and EEG source analysis. In addition the covariance matrix of the estimates is derived. Simulation studies with a concentric spheres model and a more realistic boundary element model indicate that this method has several advantages, even if only a few EEG sensors are added to a MEG configuration. First, a simultaneous analysis profits from the 'preferred' location directions of MEG and EEG. Second, deep sources can be estimated quite accurately, which is an advantage compared to MEG. Third, superficial sources profit from accurate MEG location and from accurate EEG moment. Fourth, the radial source component can be estimated, which is an advantage compared to MEG. Fifth, the conductivities can be estimated. It is shown that conductivity estimation gives a substantial increase in precision, even if the conductivities are not identified appropriately. An illustrative analysis of empirical data supports these findings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".