MétaCan
Menu
Back to cohort
Record W2035385895 · doi:10.1109/tbme.2011.2108295

Online Removal of Eye Movement and Blink EEG Artifacts Using a High-Speed Eye Tracker

2011· article· en· W2035385895 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2011
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsNeil Squire SocietyUniversity of British Columbia
Fundersnot available
KeywordsEye movementComputer visionElectrooculographyElectroencephalographyEye trackingArtificial intelligenceComputer sciencePsychologyNeuroscience

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.725
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.244
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it