Optimizing MEG-EEG Mapping in Resource-Constrained Non-Intrusive Bio-Magnetic Sensing Systems: A Data-Driven Approach
Bibliographic record
Abstract
While Magnetoencephalography (MEG) and electroencephalography (EEG) are well-known neuroimaging techniques to capture a myriad of brain activities and stimulations, accessing conventional M/EEG devices is challenging. This is because of the bulky nature of the MEG machines, the need for magnetic shielding and cooling system, intrusive EEG electrodes, and various other complications involving preparing these devices to guarantee a clinical-grade signal acquisition. To address these issues, in this paper, we consider bio-magnetic sensing with emerging Magnetic Tunnel Junction (MTJ) sensors operating at room temperature that can map the sensed MEG to EEG signals, which can be helpful in this domain. However, such ultra-sensitive sensors are resource-constrained, and incorporating such MEG-EEG mapping needs to be optimized to balance the accuracy and computational/energy trade-off. Therefore, we adopt a data-centric approach to address this optimization problem. Furthermore, we conduct rigorous comparative analytics on prominent machine/deep learning models on a publicly available dataset to establish a baseline proof-of-concept that can be seamlessly integrated with the considered bio-magnetic sensing systems. Our research unlocks the possibility for real-time monitoring of brain activities and abnormality detection away from the clinical environment and complex hospital settings. Moreover, the compact size and low-power requirements for the considered MTJ sensor make it compatible with IoT and wearable devices.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.002 | 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".