A new and general approach to signal denoising and eye movement classification based on segmented linear regression

Research output: Contribution to journalArticleScientificpeer-review

Abstract

We introduce a conceptually novel method for eye-movement signal analysis. The method is general in that it does not place severe restrictions on sampling frequency, measurement noise or subject behavior. Event identification is based on segmentation that simultaneously denoises the signal and determines event boundaries. The full gaze position time-series is segmented into an approximately optimal piecewise linear function in O(n) time. Gaze feature parameters for classification into fixations, saccades, smooth pursuits and post-saccadic oscillations are derived from human labeling in a data-driven manner. The range of oculomotor events identified and the powerful denoising performance make the method useable for both low-noise controlled laboratory settings and high-noise complex field experiments. This is desirable for harmonizing the gaze behavior (in the wild) and oculomotor event identification (in the laboratory) approaches to eye movement behavior. Denoising and classification performance are assessed using multiple datasets. Full open source implementation is included.
Original languageEnglish
Article number17726
JournalScientific Reports
Volume7
Pages (from-to)1-13
Number of pages13
ISSN2045-2322
DOIs
Publication statusPublished - 18 Dec 2017
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 6162 Cognitive science
  • open data
  • open source software
  • eye tracking
  • eye movements
  • signal processing
  • denoising
  • fixation
  • saccades
  • smooth pursuit
  • post-saccadic oscillation

Cite this

@article{86771c0a594f4d4aa7c79d75cdd65d33,
title = "A new and general approach to signal denoising and eye movement classification based on segmented linear regression",
abstract = "We introduce a conceptually novel method for eye-movement signal analysis. The method is general in that it does not place severe restrictions on sampling frequency, measurement noise or subject behavior. Event identification is based on segmentation that simultaneously denoises the signal and determines event boundaries. The full gaze position time-series is segmented into an approximately optimal piecewise linear function in O(n) time. Gaze feature parameters for classification into fixations, saccades, smooth pursuits and post-saccadic oscillations are derived from human labeling in a data-driven manner. The range of oculomotor events identified and the powerful denoising performance make the method useable for both low-noise controlled laboratory settings and high-noise complex field experiments. This is desirable for harmonizing the gaze behavior (in the wild) and oculomotor event identification (in the laboratory) approaches to eye movement behavior. Denoising and classification performance are assessed using multiple datasets. Full open source implementation is included.",
keywords = "6162 Cognitive science, open data, open source software, eye tracking, eye movements, signal processing, denoising, fixation, saccades, smooth pursuit, post-saccadic oscillation",
author = "Pekkanen, {Jami Joonas Olavi} and Lappi, {Mikko Otto Tapio}",
year = "2017",
month = "12",
day = "18",
doi = "10.1038/s41598-017-17983-x",
language = "English",
volume = "7",
pages = "1--13",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",

}

A new and general approach to signal denoising and eye movement classification based on segmented linear regression. / Pekkanen, Jami Joonas Olavi; Lappi, Mikko Otto Tapio.

In: Scientific Reports, Vol. 7, 17726, 18.12.2017, p. 1-13.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - A new and general approach to signal denoising and eye movement classification based on segmented linear regression

AU - Pekkanen, Jami Joonas Olavi

AU - Lappi, Mikko Otto Tapio

PY - 2017/12/18

Y1 - 2017/12/18

N2 - We introduce a conceptually novel method for eye-movement signal analysis. The method is general in that it does not place severe restrictions on sampling frequency, measurement noise or subject behavior. Event identification is based on segmentation that simultaneously denoises the signal and determines event boundaries. The full gaze position time-series is segmented into an approximately optimal piecewise linear function in O(n) time. Gaze feature parameters for classification into fixations, saccades, smooth pursuits and post-saccadic oscillations are derived from human labeling in a data-driven manner. The range of oculomotor events identified and the powerful denoising performance make the method useable for both low-noise controlled laboratory settings and high-noise complex field experiments. This is desirable for harmonizing the gaze behavior (in the wild) and oculomotor event identification (in the laboratory) approaches to eye movement behavior. Denoising and classification performance are assessed using multiple datasets. Full open source implementation is included.

AB - We introduce a conceptually novel method for eye-movement signal analysis. The method is general in that it does not place severe restrictions on sampling frequency, measurement noise or subject behavior. Event identification is based on segmentation that simultaneously denoises the signal and determines event boundaries. The full gaze position time-series is segmented into an approximately optimal piecewise linear function in O(n) time. Gaze feature parameters for classification into fixations, saccades, smooth pursuits and post-saccadic oscillations are derived from human labeling in a data-driven manner. The range of oculomotor events identified and the powerful denoising performance make the method useable for both low-noise controlled laboratory settings and high-noise complex field experiments. This is desirable for harmonizing the gaze behavior (in the wild) and oculomotor event identification (in the laboratory) approaches to eye movement behavior. Denoising and classification performance are assessed using multiple datasets. Full open source implementation is included.

KW - 6162 Cognitive science

KW - open data

KW - open source software

KW - eye tracking

KW - eye movements

KW - signal processing

KW - denoising

KW - fixation

KW - saccades

KW - smooth pursuit

KW - post-saccadic oscillation

U2 - 10.1038/s41598-017-17983-x

DO - 10.1038/s41598-017-17983-x

M3 - Article

VL - 7

SP - 1

EP - 13

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

M1 - 17726

ER -