Using electrooculography to track closed-eye movements.
Bibliographic record
Abstract
There are several areas in the study of visual cognition—including memory, imagery, and human-machine interaction—where researchers are interested in how the eyes move behind closed eyelids. However, reliably and affordably measuring closed-eye movements has proven elusive. Electrooculography (EOG) offers a low-cost solution to monitoring closed-eye gaze position, but it is not without its challenges. To determine the direction and amplitude of eye movements, the electrical potentials measured by EOG somehow must be calibrated with the angular displacement of the eye. EOG is also susceptible to noise arising from various sources, such as electromyographic activity and electrode impedance. Here we describe a method for estimating a corrected EOG signal by calibrating it with an industry-standard, pupil-corneal reflection (PCR) eye tracker. First, data were collected while simultaneously using both eye-tracking techniques as participants performed a simple horizontal saccade task with their eyes open under conditions of normal illumination and complete darkness. The EOG signal, when using only a standard calibration procedure, was less precise than that of PCR and tended to overestimate saccadic amplitude. We applied robust regression methods to the EOG and PCR data recorded in normal illumination to estimate a calibration factor to adjust the EOG signal acquired in darkness. This adjustment yielded an EOG-based measure of saccade end-points that was more comparable—in both accuracy and precision—to that obtained from the PCR data. Having validated this calibration procedure, we applied it to compute an adjusted EOG measure of saccadic amplitude in another condition where participants’ eyes were closed. This adjustment likely improved our measurement of how accurately participants were able to execute closed-eye movements to remembered target locations. We propose that the refinement and application of this methodology can advance research under conditions where researchers would like to measure the kinematics of closed-eye movements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".