Advanced

Using machine learning to detect events in eye-tracking data

Zemblys, Raimondas; Niehorster, Diederick C LU ; Komogortsev, Oleg and Holmqvist, Kenneth LU (2017) In Behavior Research Methods & Instrumentation
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.

(Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
Eye movements , Event detection , Machine learning , Fixations , Saccades
in
Behavior Research Methods & Instrumentation
pages
22 pages
publisher
The Psychonomic Society
external identifiers
  • scopus:85013675310
ISSN
1554-351X
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
2018-01-07 11:52:50
@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         = {1554-351X},
  keyword      = {Eye movements ,Event detection ,Machine learning ,Fixations ,Saccades},
  language     = {eng},
  month        = {02},
  pages        = {22},
  publisher    = {The Psychonomic Society},
  series       = {Behavior Research Methods & Instrumentation},
  title        = {Using machine learning to detect events in eye-tracking data},
  url          = {http://dx.doi.org/10.3758/s13428-017-0860-3},
  year         = {2017},
}