On electromagnetic head-surface digitization in MEG and EEG
Bibliographic record
Abstract
Abstract In magnetoencephalographic (MEG) and electroencephalographic (EEG) studies, the accuracy of the head digitization impacts the co-registration between MEG/EEG and structural magnetic resonance imaging (MRI) data. The co-registration accuracy in turn affects the spatial accuracy in neural source imaging. Precisely digitized head-surface (scalp) points do not only improve the co-registration but can also be used to deform a template MRI to create an individualized-template MRI that can be used for conductivity modeling in MEG/EEG source imaging if the individual’s structural MRI is unavailable. Although optical scanners have been introduced for digitization, especially for dense EEG electrode sets, electromagnetic tracking (EMT) systems (particularly Fastrak®, Polhemus Inc., Colchester, VT, USA) have been the most often applied solution for digitization in MEG and EEG acquisition. EMT systems may occasionally suffer from ambient electromagnetic interference which makes it challenging to achieve (sub-)millimeter digitization accuracy. The current study i) evaluated the performance of the Fastrak® EMT system under different conditions in MEG/EEG digitization, and ii) explores the usability of two alternative EMT systems (Aurora®, NDI, Waterloo, ON, Canada; Fastrak® with a short-range transmitter) for digitization. Tracking fluctuation, digitization accuracy, and robustness of the systems were evaluated in several test cases using test frames and human head models. The digitization performance of the two alternative systems was compared against the Fastrak® system. The results showed that the Fastrak® system is accurate and robust for MEG/EEG digitization if the recommended operating conditions are met. The Fastrak® with the short-range transmitter shows comparatively higher digitization error if digitization is not carried out very close to the transmitter. The study also evinces that the Aurora® system can be used for MEG/EEG digitization. Its real-time error estimation feature can potentially improve digitization accuracy, albeit some modifications would be required to make the system a practical and easy-to-use digitizer.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".