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NoLiTiA: An Open-Source Toolbox for Non-linear Time Series Analysis
In many scientific fields including neuroscience, climatology or physics, complex relationships can be described most parsimoniously by non-linear mechanics. Despite their relevance, many neuroscientists still apply linear estimates in order to evaluate complex interactions. This is partially due to...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9263366/ https://www.ncbi.nlm.nih.gov/pubmed/35811996 http://dx.doi.org/10.3389/fninf.2022.876012 |
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author | Weber, Immo Oehrn, Carina R. |
author_facet | Weber, Immo Oehrn, Carina R. |
author_sort | Weber, Immo |
collection | PubMed |
description | In many scientific fields including neuroscience, climatology or physics, complex relationships can be described most parsimoniously by non-linear mechanics. Despite their relevance, many neuroscientists still apply linear estimates in order to evaluate complex interactions. This is partially due to the lack of a comprehensive compilation of non-linear methods. Available packages mostly specialize in only one aspect of non-linear time-series analysis and most often require some coding proficiency to use. Here, we introduce NoLiTiA, a free open-source MATLAB toolbox for non-linear time series analysis. In comparison to other currently available non-linear packages, NoLiTiA offers (1) an implementation of a broad range of classic and recently developed methods, (2) an implementation of newly proposed spatially and time-resolved recurrence amplitude analysis and (3) an intuitive environment accessible even to users with little coding experience due to a graphical user interface and batch-editor. The core methodology derives from three distinct fields of complex systems theory, including dynamical systems theory, recurrence quantification analysis and information theory. Besides established methodology including estimation of dynamic invariants like Lyapunov exponents and entropy-based measures, such as active information storage, we include recent developments of quantifying time-resolved aperiodic oscillations. In general, the toolbox will make non-linear methods accessible to the broad neuroscientific community engaged in time series processing. |
format | Online Article Text |
id | pubmed-9263366 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92633662022-07-09 NoLiTiA: An Open-Source Toolbox for Non-linear Time Series Analysis Weber, Immo Oehrn, Carina R. Front Neuroinform Neuroscience In many scientific fields including neuroscience, climatology or physics, complex relationships can be described most parsimoniously by non-linear mechanics. Despite their relevance, many neuroscientists still apply linear estimates in order to evaluate complex interactions. This is partially due to the lack of a comprehensive compilation of non-linear methods. Available packages mostly specialize in only one aspect of non-linear time-series analysis and most often require some coding proficiency to use. Here, we introduce NoLiTiA, a free open-source MATLAB toolbox for non-linear time series analysis. In comparison to other currently available non-linear packages, NoLiTiA offers (1) an implementation of a broad range of classic and recently developed methods, (2) an implementation of newly proposed spatially and time-resolved recurrence amplitude analysis and (3) an intuitive environment accessible even to users with little coding experience due to a graphical user interface and batch-editor. The core methodology derives from three distinct fields of complex systems theory, including dynamical systems theory, recurrence quantification analysis and information theory. Besides established methodology including estimation of dynamic invariants like Lyapunov exponents and entropy-based measures, such as active information storage, we include recent developments of quantifying time-resolved aperiodic oscillations. In general, the toolbox will make non-linear methods accessible to the broad neuroscientific community engaged in time series processing. Frontiers Media S.A. 2022-06-24 /pmc/articles/PMC9263366/ /pubmed/35811996 http://dx.doi.org/10.3389/fninf.2022.876012 Text en Copyright © 2022 Weber and Oehrn. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Weber, Immo Oehrn, Carina R. NoLiTiA: An Open-Source Toolbox for Non-linear Time Series Analysis |
title | NoLiTiA: An Open-Source Toolbox for Non-linear Time Series Analysis |
title_full | NoLiTiA: An Open-Source Toolbox for Non-linear Time Series Analysis |
title_fullStr | NoLiTiA: An Open-Source Toolbox for Non-linear Time Series Analysis |
title_full_unstemmed | NoLiTiA: An Open-Source Toolbox for Non-linear Time Series Analysis |
title_short | NoLiTiA: An Open-Source Toolbox for Non-linear Time Series Analysis |
title_sort | nolitia: an open-source toolbox for non-linear time series analysis |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9263366/ https://www.ncbi.nlm.nih.gov/pubmed/35811996 http://dx.doi.org/10.3389/fninf.2022.876012 |
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