A Wireless Flexible Electrooculogram Monitoring System With Printed Electrodes
Bibliographic record
Abstract
Electroocugraphy (EOG) is a popular method of measuring biopotentials developed across the eyes during eye activities such as eye-blinking, vertical, and horizontal eye-movements. The measured signal is called electrooculogram (EOG) and has been known to be used in behavioral studies, cognitive neuroscience and sleep monitoring. In this work a single channel wearable wireless EOG monitoring system is presented. The entire system is implemented on a double sided polymide flexible substrate. The recording silver electrodes are printed on the bottom side of the substrate whereas the EOG signal recording and transmission circuitries are implemented on the top side of the substrate with printed silver traces. The system is run by a rechargeable battery and uses a BLE 5.0 transceiver for wireless connectivity. Design considerations for the wearable EOG monitoring system are discussed in details. The system performance is validated by successfully monitoring different eye movements with it. Additionally, comparison between the EOG signals observed using the printed silver electrodes and commercial gold electrodes of same dimensions demonstrate that the printed electrodes provide similar EOG signal amplitude like the commercial gold electrodes. With a 5.2 gram mass and flexibility the system has potential for monitoring EOG signal without causing discomfort to the wearer.
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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 | 0.001 | 0.001 |
| 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".