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Searching for Chaos Evidence in Eye Movement Signals
Most naturally-occurring physical phenomena are examples of nonlinear dynamic systems, the functioning of which attracts many researchers seeking to unveil their nature. The research presented in this paper is aimed at exploring eye movement dynamic features in terms of the existence of chaotic natu...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512232/ https://www.ncbi.nlm.nih.gov/pubmed/33265121 http://dx.doi.org/10.3390/e20010032 |
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author | Harezlak, Katarzyna Kasprowski, Pawel |
author_facet | Harezlak, Katarzyna Kasprowski, Pawel |
author_sort | Harezlak, Katarzyna |
collection | PubMed |
description | Most naturally-occurring physical phenomena are examples of nonlinear dynamic systems, the functioning of which attracts many researchers seeking to unveil their nature. The research presented in this paper is aimed at exploring eye movement dynamic features in terms of the existence of chaotic nature. Nonlinear time series analysis methods were used for this purpose. Two time series features were studied: fractal dimension and entropy, by utilising the embedding theory. The methods were applied to the data collected during the experiment with “jumping point” stimulus. Eye movements were registered by means of the Jazz-novo eye tracker. One thousand three hundred and ninety two (1392) time series were defined, based on the horizontal velocity of eye movements registered during imposed, prolonged fixations. In order to conduct detailed analysis of the signal and identify differences contributing to the observed patterns of behaviour in time scale, fractal dimension and entropy were evaluated in various time series intervals. The influence of the noise contained in the data and the impact of the utilized filter on the obtained results were also studied. The low pass filter was used for the purpose of noise reduction with a 50 Hz cut-off frequency, estimated by means of the Fourier transform and all concerned methods were applied to time series before and after noise reduction. These studies provided some premises, which allow perceiving eye movements as observed chaotic data: characteristic of a space-time separation plot, low and non-integer time series dimension, and the time series entropy characteristic for chaotic systems. |
format | Online Article Text |
id | pubmed-7512232 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75122322020-11-09 Searching for Chaos Evidence in Eye Movement Signals Harezlak, Katarzyna Kasprowski, Pawel Entropy (Basel) Article Most naturally-occurring physical phenomena are examples of nonlinear dynamic systems, the functioning of which attracts many researchers seeking to unveil their nature. The research presented in this paper is aimed at exploring eye movement dynamic features in terms of the existence of chaotic nature. Nonlinear time series analysis methods were used for this purpose. Two time series features were studied: fractal dimension and entropy, by utilising the embedding theory. The methods were applied to the data collected during the experiment with “jumping point” stimulus. Eye movements were registered by means of the Jazz-novo eye tracker. One thousand three hundred and ninety two (1392) time series were defined, based on the horizontal velocity of eye movements registered during imposed, prolonged fixations. In order to conduct detailed analysis of the signal and identify differences contributing to the observed patterns of behaviour in time scale, fractal dimension and entropy were evaluated in various time series intervals. The influence of the noise contained in the data and the impact of the utilized filter on the obtained results were also studied. The low pass filter was used for the purpose of noise reduction with a 50 Hz cut-off frequency, estimated by means of the Fourier transform and all concerned methods were applied to time series before and after noise reduction. These studies provided some premises, which allow perceiving eye movements as observed chaotic data: characteristic of a space-time separation plot, low and non-integer time series dimension, and the time series entropy characteristic for chaotic systems. MDPI 2018-01-07 /pmc/articles/PMC7512232/ /pubmed/33265121 http://dx.doi.org/10.3390/e20010032 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Harezlak, Katarzyna Kasprowski, Pawel Searching for Chaos Evidence in Eye Movement Signals |
title | Searching for Chaos Evidence in Eye Movement Signals |
title_full | Searching for Chaos Evidence in Eye Movement Signals |
title_fullStr | Searching for Chaos Evidence in Eye Movement Signals |
title_full_unstemmed | Searching for Chaos Evidence in Eye Movement Signals |
title_short | Searching for Chaos Evidence in Eye Movement Signals |
title_sort | searching for chaos evidence in eye movement signals |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512232/ https://www.ncbi.nlm.nih.gov/pubmed/33265121 http://dx.doi.org/10.3390/e20010032 |
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