Online Removal of Eye Movement and Blink EEG Artifacts Using a High-Speed Eye Tracker
Bibliographic record
Abstract
A novel approach is presented for using an eye tracker-based reference instead of EOG for methods that require an EOG reference to remove ocular artifacts (OA) from EEG. It uses a high-speed eye tracker and a new online algorithm for extracting the time course of a blink from eye tracker images to remove both eye movement and blink artifacts. It eliminates the need for EOG electrodes attached to the face, which is critical for practical daily applications. The ability of two adaptive filters (RLS and H^ ) to remove OA is measured using: 1) EOG; 2) frontal EEG only (fEEG); and 3) the eye tracker with frontal EEG (ET + fEEG) as reference inputs. The results are compared for different eye movements and blinks of varying amplitudes at electrodes across the scalp. Both the RLS and H^ methods were shown to benefit from using the proposed eye tracker-based reference (ET + fEEG) instead of either an EOG reference or a reference based on frontal EEG alone.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".