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A data-driven algorithm for offline pupil signal preprocessing and eyeblink detection in low-speed eye-tracking

Marco Pedrotti¹, Shengguang Lei, Jeronimo Dzaack

¹LIDEA-InterDepartmental Laboratory of Applied Ergonomics, Università di Torino, Torino, Italy. marco.pedrotti@unito.it

Behavior Research Methods

|February 9, 2011

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This study introduces the Identification-Artifact Correction (I-AC) algorithm for precise eyeblink detection in 50-Hz eye-tracking data. The data-driven method accurately identifies blinks, improving the reliability of eye-tracking analyses.

Area of Science:

Ophthalmology
Neuroscience
Human-Computer Interaction

Background:

Accurate event detection in eye-tracking data is crucial for research.
Existing event detection algorithms often lack detailed reporting, impacting analysis reliability.
Blinks are common artifacts that require precise identification and correction.

Purpose of the Study:

To develop a data-driven algorithm for accurate eyeblink detection in 50-Hz eye-tracking data.
To correct blink-related artifacts in pupil diameter data.
To improve the reliability of higher-level analyses in eye-tracking studies.

Main Methods:

Developed the Identification-Artifact Correction (I-AC) algorithm.
Implemented data-driven thresholds for artifact correction.

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Defined blink parameters based on visual suppression research.

Validated performance against simultaneous eye images and gaze data.

Main Results:

Achieved 97% correct detection of eyeblinks.
Provided reliable pupil diameter data after artifact correction.
Successfully estimated blink onset and offset.

Conclusions:

The I-AC algorithm offers a robust and accurate method for eyeblink detection.
Improved artifact correction enhances the quality of eye-tracking data.
This method facilitates more reliable downstream analyses in eye-tracking research.