title: A data-driven algorithm for offline pupil signal preprocessing and eyeblink detection in low-speed eye-tracking protocols.
abstract: Event detection is the conversion of raw eye-tracking data into events--such as fixations, saccades, glissades, blinks, and so forth--that are relevant for researchers. In eye-tracking studies, event detection algorithms can have a serious impact on higher level analyses, although most studies do not accurately report their settings. We developed a data-driven eyeblink detection algorithm (Identification-Artifact Correction [I-AC]) for 50-Hz eye-tracking protocols. I-AC works by first correcting blink-related artifacts within pupil diameter values and then estimating blink onset and offset. Artifact correction is achieved with data-driven thresholds, and more reliable pupil data are output. Blink parameters are defined according to previous studies on blink-related visual suppression. Blink detection performance was tested with experimental data by visually checking the actual correspondence between I-AC output and participants' eye images, recorded by the eyetracker simultaneously with gaze data. Results showed a 97% correct detection percentage.
authors.affiliation: LIDEA-InterDepartmental Laboratory of Applied Ergonomics, Università di Torino, Torino, Italy. marco.pedrotti@unito.it
authors.forename: Marco
authors.initials: M
authors.lastname: Pedrotti
authors.forename: Shengguang
authors.initials: S
authors.lastname: Lei
authors.forename: Jeronimo
authors.initials: J
authors.lastname: Dzaack
authors.forename: Matthias
authors.initials: M
authors.lastname: Rötting
mesh_terms: D000465:Algorithms; D001767:Blinking; D053483:Eye Movement Measurements / Q000295:instrumentation*; D005133:Eye Movements; D006801:Humans; D011680:Pupil; D012815:Signal Processing, Computer-Assisted