Fetal MEG Redistribution by Projection Operators
Bibliographic record
Abstract
The fetal magnetoencephalogram (fMEG) is measured in the presence of large interference from the maternal and fetal magnetocardiograms. This interference can be efficiently attenuated by orthogonal projection of the corresponding spatial vectors. However, the projection operators redistribute the fMEG signal among sensors. Although redistribution can be readily accounted for in the forward solution, visual interpretation of the fMEG signal topography is made difficult. We have devised a general, model-independent method for correction of the redistribution effect that utilizes the assumption that we know in which channels the fMEG should be negligible (such channels are distant from the known fetal head position). In a simplified case where the fMEG can be explained by equivalent current dipoles, the correction can also be obtained from fitting the dipoles to the fMEG signal. The corrected fMEG signal topography then corresponds to the dipole forward solution, but without orthogonal projection. We illustrate the redistribution correction on an example of experimentally measured flash evoked fMEG.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".