Using machine learning to detect events in eye-tracking data
(2018) In Behavior Research Methods 50(1). p.160-181- 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... (More)
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.
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- author
- Zemblys, Raimondas ; Niehorster, Diederick C LU ; Komogortsev, Oleg and Holmqvist, Kenneth LU
- organization
- publishing date
- 2018-02
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Eye movements, Event detection, Machine learning, Fixations, Saccades
- in
- Behavior Research Methods
- volume
- 50
- issue
- 1
- pages
- 160 - 181
- publisher
- Springer
- external identifiers
-
- pmid:28233250
- scopus:85013675310
- ISSN
- 1554-3528
- DOI
- 10.3758/s13428-017-0860-3
- language
- English
- LU publication?
- yes
- id
- fbf97ced-68b9-47e2-946f-26ea78d765e6
- date added to LUP
- 2017-02-27 09:16:38
- date last changed
- 2024-09-16 19:55:11
@article{fbf97ced-68b9-47e2-946f-26ea78d765e6, abstract = {{<p>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.</p>}}, author = {{Zemblys, Raimondas and Niehorster, Diederick C and Komogortsev, Oleg and Holmqvist, Kenneth}}, issn = {{1554-3528}}, keywords = {{Eye movements; Event detection; Machine learning; Fixations; Saccades}}, language = {{eng}}, number = {{1}}, pages = {{160--181}}, publisher = {{Springer}}, series = {{Behavior Research Methods}}, title = {{Using machine learning to detect events in eye-tracking data}}, url = {{http://dx.doi.org/10.3758/s13428-017-0860-3}}, doi = {{10.3758/s13428-017-0860-3}}, volume = {{50}}, year = {{2018}}, }