title: Using machine learning to detect events in eye-tracking data.
abstract: Event detection is a challenging stage in eye movement data analysis. A major drawback of current event detection methods is that parameters have to be adjusted based on eye movement data quality. Here we show that a fully automated classification of raw gaze samples as belonging to fixations, saccades, or other oculomotor events can be achieved using a machine-learning approach. Any already manually or algorithmically detected events can be used to train a classifier to produce similar classification of other data without the need for a user to set parameters. In this study, we explore the application of random forest machine-learning technique for the detection of fixations, saccades, and post-saccadic oscillations (PSOs). In an effort to show practical utility of the proposed method to the applications that employ eye movement classification algorithms, we provide an example where the method is employed in an eye movement-driven biometric application. We conclude that machine-learning techniques lead to superior detection compared to current state-of-the-art event detection algorithms and can reach the performance of manual coding.
authors.affiliation: Department of Engineering, Siauliai University, Siauliai, Lithuania. r.zemblys@tf.su.lt.|Humanities Laboratory, Lund University, Lund, Sweden. r.zemblys@tf.su.lt.
authors.forename: Raimondas
authors.initials: R
authors.lastname: Zemblys
authors.affiliation: Humanities Laboratory and Department of Psychology, Lund University, Lund, Sweden.|Institute for Psychology, University of Muenster, Muenster, Germany.
authors.forename: Diederick C
authors.initials: DC
authors.lastname: Niehorster
authors.affiliation: Department of Computer Science, Texas State University, San Marcos, TX, USA.
authors.forename: Oleg
authors.initials: O
authors.lastname: Komogortsev
authors.affiliation: Humanities Laboratory, Lund University, Lund, Sweden.|UPSET, NWU Vaal, Vanderbijlpark, South Africa.
authors.forename: Kenneth
authors.initials: K
authors.lastname: Holmqvist
mesh_terms: D000465:Algorithms; D035841:Behavioral Research; D001699:Biometry / Q000295:instrumentation / Q000379:methods; D005133:Eye Movements / Q000502:physiology*; D006801:Humans; D000069550:Machine Learning; D013647:Task Performance and Analysis
keywords: Event detection; Eye movements; Fixations; Machine learning; Saccades