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

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 deter...

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Autores principales: Pekkanen, Jami, Lappi, Otto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5735175/
https://www.ncbi.nlm.nih.gov/pubmed/29255207
http://dx.doi.org/10.1038/s41598-017-17983-x
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author Pekkanen, Jami
Lappi, Otto
author_facet Pekkanen, Jami
Lappi, Otto
author_sort Pekkanen, Jami
collection PubMed
description 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.
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spelling pubmed-57351752017-12-21 A new and general approach to signal denoising and eye movement classification based on segmented linear regression Pekkanen, Jami Lappi, Otto Sci Rep Article 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. Nature Publishing Group UK 2017-12-18 /pmc/articles/PMC5735175/ /pubmed/29255207 http://dx.doi.org/10.1038/s41598-017-17983-x Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Pekkanen, Jami
Lappi, Otto
A new and general approach to signal denoising and eye movement classification based on segmented linear regression
title A new and general approach to signal denoising and eye movement classification based on segmented linear regression
title_full A new and general approach to signal denoising and eye movement classification based on segmented linear regression
title_fullStr A new and general approach to signal denoising and eye movement classification based on segmented linear regression
title_full_unstemmed A new and general approach to signal denoising and eye movement classification based on segmented linear regression
title_short A new and general approach to signal denoising and eye movement classification based on segmented linear regression
title_sort new and general approach to signal denoising and eye movement classification based on segmented linear regression
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5735175/
https://www.ncbi.nlm.nih.gov/pubmed/29255207
http://dx.doi.org/10.1038/s41598-017-17983-x
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