Papers by Valentina Unakafova
Frontiers in Neuroinformatics
Analysis of spike and local field potential (LFP) data is an essential part of neuroscientific re... more Analysis of spike and local field potential (LFP) data is an essential part of neuroscientific research. Today there exist many open-source toolboxes for spike and LFP data analysis implementing various functionality. Here we aim to provide a practical guidance for neuroscientists in the choice of an open-source toolbox best satisfying their needs. We overview major open-source toolboxes for spike and LFP data analysis as well as toolboxes with tools for connectivity analysis, dimensionality reduction and generalized linear modeling. We focus on comparing toolboxes functionality, statistical and visualization tools, documentation and support quality. To give a better insight, we compare and illustrate functionality of the toolboxes on open-access dataset or simulated data and make corresponding MATLAB scripts publicly available.
Analysis of spike and local field potential (LFP) data is an essential part of neuroscientific re... more Analysis of spike and local field potential (LFP) data is an essential part of neuroscientific research. Today there exist many open-source toolboxes for spike and LFP data analysis implementing various functionality. Here we aim to provide a practical guidance for neuroscientists in the choice of an open-source toolbox best satisfying their needs. We overview major open-source toolboxes for spike and LFP data analysis as well as toolboxes with tools for connectivity analysis, dimensionality reduction and generalized linear modeling. We focus on comparing toolboxes functionality, statistical and visualization tools, documentation and support quality. To give a better insight, we compare and illustrate functionality of the toolboxes on open-access dataset or simulated data and make corresponding MATLAB scripts publicly available.
On entropy, entropy-like quantities, and applications
Discrete and Continuous Dynamical Systems - Series B, 2015
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, Jan 13, 2015
Ordinal symbolic analysis opens an interesting and powerful perspective on time-series analysis. ... more Ordinal symbolic analysis opens an interesting and powerful perspective on time-series analysis. Here, we review this relatively new approach and highlight its relation to symbolic dynamics and representations. Our exposition reaches from the general ideas up to recent developments, with special emphasis on its applications to biomedical recordings. The latter will be illustrated with epilepsy data.
Physica D: Nonlinear Phenomena, 2012
Since Bandt et al. have shown that the permutation entropy and the Kolmogorov-Sinai entropy coinc... more Since Bandt et al. have shown that the permutation entropy and the Kolmogorov-Sinai entropy coincide for piecewise monotone interval maps, the relationship of both entropies for time-discrete dynamical systems is of a certain interest. The aim of this paper is a discussion of this relationship on the basis of an ordinal characterization of the Kolmogorov-Sinai entropy recently given.
Entropy, 2014
In this paper we illustrate the potential of ordinal-patterns-based methods for analysis of real-... more In this paper we illustrate the potential of ordinal-patterns-based methods for analysis of real-world data and, especially, of electroencephalogram (EEG) data. We apply already known (empirical permutation entropy, ordinal pattern distributions) and new (empirical conditional entropy of ordinal patterns, robust to noise empirical permutation entropy) methods for measuring complexity, segmentation and classification of time series.
Entropy, 2013
Permutation entropy, introduced by Bandt and Pompe, is a conceptually simple and well-interpretab... more Permutation entropy, introduced by Bandt and Pompe, is a conceptually simple and well-interpretable measure of time series complexity. In this paper, we propose efficient methods for computing it and related ordinal-patterns-based characteristics. The methods are based on precomputing values of successive ordinal patterns of order d, considering the fact that they are "overlapped" in d points, and on precomputing successive values of the permutation entropy related to "overlapping" successive time-windows. The proposed methods allow for measurement of the complexity of very large datasets in real-time.
The European Physical Journal Special Topics, 2013
In this paper we discuss the relationship between permutation entropy and Kolmogorov-Sinai entrop... more In this paper we discuss the relationship between permutation entropy and Kolmogorov-Sinai entropy in the one-dimensional case. For this, we consider partitions of the state space of a dynamical system using ordinal patterns of order (d+n−1) on the one hand, and using n-letter words of ordinal patterns of order d on the other hand. The answer to the question of how different these partitions are provides an approach to comparing the entropies.
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Papers by Valentina Unakafova