A Wearable Electrooculogram System with Parallel Motion Artifact Sensing and Reduction
Bibliographic record
Abstract
Electrooculogram signal is a well-known physiological metric. Electrooculogram measurements suffer from motion artifact and environmental vibrations. Such artifact are random in nature, may have large dynamic range, and may saturate the overall measurement system output. In this manuscript, we present a single channel, wireless, flexible EOG monitoring system which has capability to reduce motion artifact. The system uses dry non-contact electrodes which makes it mountable with minimal assistance required. The entire EOG system is implemented on a four-layer flexible polyimide substrate, with the EOG acquisition unit on the top layer, noncontact measurement electrodes printed on the bottom layer, circuit ground on the second layer, and active shielding on the third layer. The system uses parallel non-contact electrode pair for EOG signal detection and motion artifact reduction. The battery operated system utilizes only 56 mW of power while using a BLE 5.0 transceiver for wireless EOG data transmission. The system is designed for an effective EOG signal bandwidth of 1 Hz to 40 Hz with an effective signal gain above 35 dB over the signal bandwidth. The capability of the system for motion artifact reduction and EOG detection are experimentally validated. With only 8.75 gram weight the system does not cause any discomfort to the wearer during EOG recording.
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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) | 0.004 | 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".