ArticleEntropy (Basel, Switzerland)2018
Searching for Chaos Evidence in Eye Movement Signals.
Article in Entropy (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- What can entropy metrics tell us about the characteristics of ocular fixation trajectories?PloS one · 2024Article
- Datasets for learning of unknown characteristics of dynamical systems.Scientific data · 2023Article
- Proof of Concept of Novel Visuo-Spatial-Motor Fall Prevention Training for Old People.Geriatrics (Basel, Switzerland) · 2021Article
- Application of Time-Scale Decomposition of Entropy for Eye Movement Analysis.Entropy (Basel, Switzerland) · 2020Article
- Understanding Eye Movement Signal Characteristics Based on Their Dynamical and Fractal Features.Sensors (Basel, Switzerland) · 2019Article
- An Analysis of Entropy-Based Eye Movement Events Detection.Entropy (Basel, Switzerland) · 2019Article
- Research Frontier in Chaos Theory and Complex Networks.Entropy (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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