An Oculomotor Sensing Technique for Saccade Isolation of Eye Movements using OpenBCI
Bibliographic record
Abstract
Oculomotor sensing is used in clinical diagnosing of medical disorders, human computer/robot interaction systems and surgical planning software. Various devices are used for oculomotor sensing, namely electrooculography (EOG), video oculography (VOG), infrared oculography (IROG) and sclera coil (SC). EOG is inferior to VOG and IROG in spatial and temporal resolution but its superior in linearity, non-invasiveness, no sight disturbance, low cost and ability of recording closed eye movements. Accurate saccade isolation is important to implement reliable EOG based techniques. This work presents an inexpensive and accurate signal processing technique to extract saccade information. Signal filtering, isolation and calibration are implemented to extract saccade information from the raw signal. The proposed technique showed a significant improvement with respect to other approaches (used for noise filtering) found in literature.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".