NoLiTiA: A Toolbox for Analyzing Complex Relationships

Published on June 24, 2022

Just as a puzzling jigsaw can be solved by fitting together unique-shaped pieces, complex relationships in scientific fields such as neuroscience, climatology, and physics can be understood by using non-linear mechanics. However, many scientists still rely on linear estimates, like using a simple ruler rather than a complicated calculator. The reason? There has been a lack of comprehensive tools for non-linear time series analysis. But fear not! We now have NoLiTiA, an open-source MATLAB toolbox that brings together a wide range of classic and cutting-edge methods for understanding non-linear relationships. NoLiTiA offers an intuitive graphical user interface and batch-editor, making it accessible even to those without much coding experience. With its foundations in dynamical systems theory, recurrence quantification analysis, and information theory, this toolbox allows scientists to estimate dynamic invariants and explore entropy-based measures to quantify complex interactions. Moreover, it introduces new techniques like spatially and time-resolved recurrence amplitude analysis. The release of NoLiTiA opens the doors to the broader neuroscience community, helping them unravel the mysteries hidden within time series data.

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.

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