A Lightweight Flexible Wireless Electrooculogram Monitoring System With Printed Gold Electrodes
Bibliographic record
Abstract
Electroocugraphy (EOG) is a simple and non-invasive method in which biopotentials developed across the eyes are measured during various eye activities such as eye blinking, winking, and horizontal/vertical eyeball movements. The measured biopotential is called the electroculogram (EOG) signal. This paper presents a single channel EOG measurement system which is implemented on a four layer flexible polymide substrate. The EOG measurement system with its signal conditioning stages is implemented on the top layer of the flexible board whereas, the EOG measurement electrodes are printed on the bottom layer of the flexible board using gold. This eliminates the requirement of external long wires during EOG monitoring. The middle two layers of the flexible substrate are used for implementing the circuit ground plane and active shielding. The entire circuit is powered by a rechargeable Li-ion coin battery. It also uses a Bluetooth 5.0 transceiver module to send the EOG data wirelessly. The system is designed for an effective EOG signal bandwidth of 1.6 Hz to 47 Hz with an effective signal gain above 68.5 dB over the signal bandwidth. The system also has an excellent common-mode rejection ratio (CMRR) response above 70 dB. The system is validated with eight healthy subjects for the detection of different eye activities with an accuracy of 77.08 %. The mass of the entire flexible board along with its battery is only 7.7 g. Such light mass, flexible substrate, and integrated printed electrodes make this EOG monitoring prototype an ideal unit for long term monitoring of biopotentials, without causing any 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 | 0.000 |
| 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) | 0.002 | 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".