Optimal configuration of on-scalp OPMs with fixed channel counts
Bibliographic record
Abstract
Recent technological developments have brought optically pumped magnetometers (OPMs) within reach of the larger neuroscientific community. The current state-of-the-art consists of whole-head systems that measure the magnetic field at >100 locations. OPM sensors can be constructed to measure the field in either 1, 2, or 3 orientations. Consequently, the number of channels can differ from the number of sensors. This allows for magnetoencephalography (MEG) system designs with multiple measurement orientations at fewer locations, many locations with fewer orientations, or, ideally, many locations with multiple orientations. Yet, due to budget constraints, starting OPM groups are typically getting fewer sensors than what could, in principle, be accommodated in a whole head helmet-like arrangement. Furthermore, implementing multiple orientations in a single sensor comes at a cost and hardware companies are still optimizing the trade-offs between sensor designs. To guide the OPM systems design, it is relevant to know the optimal spatial distribution and sensing orientation of OPMs. We performed a simulation study in which we kept the total number of channels constant. We compared 3 synthetic 192-channel OPM arrays that were composed of either monoaxial, biaxial or triaxial sensors, where the sensors were placed at either 192, 96, or 64 measurement locations, respectively. We simulated multiple instances of an MEG signal due to a dipolar source in the brain, contaminated by various combinations of noise, considering sensor noise, brain noise, and noise induced by head (and sensor) movements in the residual ambient magnetic field. An optimal design of the MEG system serves both to record the activity of the brain, as well as the environmental noise that is to be suppressed. We performed dipole fits and evaluated the localization error and the amplitude of the estimated dipole moment. We cleaned the data using various spatio(temporal) cleaning strategies prior to fitting the dipoles. Our observations confirm earlier work, in that 1) the sensing orientation radial to the head is in general more optimal to pick up activity from the brain than tangential directions, but that 2) adding sensing orientations tangential to the head surface helps in suppressing ambient noise sources. Yet, we did not observe a clear improvement comparing triaxial with biaxial OPMs. Given that triaxial sensing may come at the expense of reduced spatial sampling over the head and reduced signal-to-noise for individual channels, we conclude that, given a fixed number of channels, biaxial sensors may be preferred with the currently available technology.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".