دورية أكاديمية

NoLiTiA: An Open-Source Toolbox for Non-linear Time Series Analysis.

التفاصيل البيبلوغرافية
العنوان: NoLiTiA: An Open-Source Toolbox for Non-linear Time Series Analysis.
المؤلفون: Weber I; Department of Neurology, Philipps University of Marburg, Marburg, Germany., Oehrn CR; Department of Neurology, Philipps University of Marburg, Marburg, Germany.; Center for Mind, Brain and Behavior, Philipps University of Marburg, Marburg, Germany.
المصدر: Frontiers in neuroinformatics [Front Neuroinform] 2022 Jun 24; Vol. 16, pp. 876012. Date of Electronic Publication: 2022 Jun 24 (Print Publication: 2022).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101477957 Publication Model: eCollection Cited Medium: Print ISSN: 1662-5196 (Print) Linking ISSN: 16625196 NLM ISO Abbreviation: Front Neuroinform Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Lausanne, Switzerland : Frontiers Research Foundation, 2007-
مستخلص: 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.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2022 Weber and Oehrn.)
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فهرسة مساهمة: Keywords: dynamical system; information theory; neural time series; non-linear; recurrence analysis
تواريخ الأحداث: Date Created: 20220711 Latest Revision: 20231102
رمز التحديث: 20231102
مُعرف محوري في PubMed: PMC9263366
DOI: 10.3389/fninf.2022.876012
PMID: 35811996
قاعدة البيانات: MEDLINE
الوصف
تدمد:1662-5196
DOI:10.3389/fninf.2022.876012