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Noise-robust fixation detection in eye movement data : Identification by two-means clustering (I2MC)

Hessels, Roy S.; Niehorster, Diederick C LU ; Kemner, Chantal and Hooge, Ignace T C (2016) In Behavior Research Methods & Instrumentation p.1-22
Abstract

Eye-tracking research in infants and older children has gained a lot of momentum over the last decades. Although eye-tracking research in these participant groups has become easier with the advance of the remote eye-tracker, this often comes at the cost of poorer data quality than in research with well-trained adults (Hessels, Andersson, Hooge, Nyström, & Kemner Infancy, 20, 601-633, 2015; Wass, Forssman, & Leppänen Infancy, 19, 427-460, 2014). Current fixation detection algorithms are not built for data from infants and young children. As a result, some researchers have even turned to hand correction of fixation detections (Saez de Urabain, Johnson, & Smith Behavior Research Methods, 47, 53-72, 2015). Here we introduce a... (More)

Eye-tracking research in infants and older children has gained a lot of momentum over the last decades. Although eye-tracking research in these participant groups has become easier with the advance of the remote eye-tracker, this often comes at the cost of poorer data quality than in research with well-trained adults (Hessels, Andersson, Hooge, Nyström, & Kemner Infancy, 20, 601-633, 2015; Wass, Forssman, & Leppänen Infancy, 19, 427-460, 2014). Current fixation detection algorithms are not built for data from infants and young children. As a result, some researchers have even turned to hand correction of fixation detections (Saez de Urabain, Johnson, & Smith Behavior Research Methods, 47, 53-72, 2015). Here we introduce a fixation detection algorithm-identification by two-means clustering (I2MC)-built specifically for data across a wide range of noise levels and when periods of data loss may occur. We evaluated the I2MC algorithm against seven state-of-the-art event detection algorithms, and report that the I2MC algorithm's output is the most robust to high noise and data loss levels. The algorithm is automatic, works offline, and is suitable for eye-tracking data recorded with remote or tower-mounted eye-trackers using static stimuli. In addition to application of the I2MC algorithm in eye-tracking research with infants, school children, and certain patient groups, the I2MC algorithm also may be useful when the noise and data loss levels are markedly different between trials, participants, or time points (e.g., longitudinal research).

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author
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
Eye-tracking , Fixation detection , Noise , Data quality , Data loss
in
Behavior Research Methods & Instrumentation
pages
1 - 22
publisher
The Psychonomic Society
external identifiers
  • scopus:84992688740
ISSN
1554-3528
DOI
10.3758/s13428-016-0822-1
language
English
LU publication?
yes
id
5de08688-385d-4625-ac6f-eedb3d229f9e
date added to LUP
2016-11-06 10:47:42
date last changed
2017-11-06 03:00:02
@article{5de08688-385d-4625-ac6f-eedb3d229f9e,
  abstract     = {<p>Eye-tracking research in infants and older children has gained a lot of momentum over the last decades. Although eye-tracking research in these participant groups has become easier with the advance of the remote eye-tracker, this often comes at the cost of poorer data quality than in research with well-trained adults (Hessels, Andersson, Hooge, Nyström, &amp; Kemner Infancy, 20, 601-633, 2015; Wass, Forssman, &amp; Leppänen Infancy, 19, 427-460, 2014). Current fixation detection algorithms are not built for data from infants and young children. As a result, some researchers have even turned to hand correction of fixation detections (Saez de Urabain, Johnson, &amp; Smith Behavior Research Methods, 47, 53-72, 2015). Here we introduce a fixation detection algorithm-identification by two-means clustering (I2MC)-built specifically for data across a wide range of noise levels and when periods of data loss may occur. We evaluated the I2MC algorithm against seven state-of-the-art event detection algorithms, and report that the I2MC algorithm's output is the most robust to high noise and data loss levels. The algorithm is automatic, works offline, and is suitable for eye-tracking data recorded with remote or tower-mounted eye-trackers using static stimuli. In addition to application of the I2MC algorithm in eye-tracking research with infants, school children, and certain patient groups, the I2MC algorithm also may be useful when the noise and data loss levels are markedly different between trials, participants, or time points (e.g., longitudinal research).</p>},
  author       = {Hessels, Roy S. and Niehorster, Diederick C and Kemner, Chantal and Hooge, Ignace T C},
  issn         = {1554-3528},
  keyword      = {Eye-tracking ,Fixation detection ,Noise ,Data quality ,Data loss},
  language     = {eng},
  month        = {10},
  pages        = {1--22},
  publisher    = {The Psychonomic Society},
  series       = {Behavior Research Methods & Instrumentation},
  title        = {Noise-robust fixation detection in eye movement data : Identification by two-means clustering (I2MC)},
  url          = {http://dx.doi.org/10.3758/s13428-016-0822-1},
  year         = {2016},
}