Using machine learning to detect events in eye-tracking data

Research output: Contribution to journalArticle


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.


External organisations
  • Siauliai University
  • University of Münster
  • Texas State University
  • North-West University
  • Lund University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Language Technology (Computational Linguistics)
  • Human Aspects of ICT
  • Human Computer Interaction


  • Eye movements , Event detection , Machine learning , Fixations , Saccades
Original languageEnglish
Pages (from-to)160-181
JournalBehavior Research Methods
Issue number1
Early online date2017 Feb 23
Publication statusPublished - 2018 Feb
Publication categoryResearch