Smart Mouthguard With Fabric Electrodes for Wireless Intraoral Electrooculogram Monitoring
Bibliographic record
Abstract
In recent years, electrooculography (EOG) has become a well-established method used in studies related to neuroscience, cognition analysis, psychological behavior, assistive technology, and sleep. The traditional placement of the EOG electrodes around the eyes tends to cause discomfort and is prone to displacement during sleep. This article presents a more comfortable and reliable way to measure the sleep EOG signals: a wireless smart mouthguard with integrated EOG sensors. The smart mouthguard is made of two ethylene-vinyl acetate (EVA) submouthguards, five conductive fabric electrodes, a flexible EOG measuring board, and a small LiPo battery, which is encapsulated in the Ecoflex for waterproofing. The device can transfer the EOG data through Bluetooth Low Energy (BLE) 5.0 to an Android app or computer terminal and can be charged wirelessly. The magnitude of skin-electrode impedance of the fabric electrodes on the mouthguard is less than 14 k$\Omega $, which is comparable to that of the standard gold EOG electrodes. The EOG measurement board has the differential gain over 19 (or 25.6 dB) and common-mode rejection ratio (CMRR) over 98.6 dB in the EOG bandwidth. The smart EOG mouthguard is validated on eight subjects for detecting EOG signal patterns of different eye activities. Results show that different eye activities can be detected from the acquired EOG signal with an accuracy of 100% for horizontal eye movements and at least 94% for vertical eye movements. As the smart mouthguard fits well and do not get displaced during sleep, the proposed smart mouthguard has potential for comfortable long-term sleep EOG monitoring.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".