Phase-Based Causality Analysis with Partial Mutual Information from Mixed Embedding

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F22%3A00558251" target="_blank" >RIV/67985807:_____/22:00558251 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1063/5.0087910" target="_blank" >http://dx.doi.org/10.1063/5.0087910</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1063/5.0087910" target="_blank" >10.1063/5.0087910</a>

Result language

angličtina

Original language name

Phase-Based Causality Analysis with Partial Mutual Information from Mixed Embedding

Original language description

Instantaneous phases extracted from multivariate time series can retain information about the relationships between the underlying mechanisms that generate the series. Although phases have been widely used in the study of nondirectional coupling and connectivity, they have not found similar appeal in the study of causality. Herein, we present a new method for phase-based causality analysis, which combines ideas from the mixed embedding technique and the information-theoretic approach to causality in coupled oscillatory systems. We then use the introduced method to investigate causality in simulated datasets of bivariate, unidirectionally paired systems from combinations of Rössler, Lorenz, van der Pol, and Mackey–Glass equations. We observe that causality analysis using the phases can capture the true causal relation for coupling strength smaller than the analysis based on the amplitudes can capture. On the other hand, the causality estimation based on the phases tends to have larger variability, which is attributed more to the phase extraction process than the actual phase-based causality method. In addition, an application on real electroencephalographic data from an experiment on elicited human emotional states reinforces the usefulness of phases in causality identification. Detection of causal relations in a system is the logical first step to accurately describe and study the system. In systems where the individual components produce time series that exhibit oscillating behavior, causality can be assessed through the phase information of the oscillations instead of the amplitude information. In this work, we propose a novel phase-based approach to detect these relations, we investigate if phases are capable of providing better detection of causality, and we identify advantages and hindrances of phase-based causality analysis.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GF21-14727K" target="_blank" >GF21-14727K: Learning Synchronization Patterns in Multivariate Neural Signals for Prediction of Response to Antidepressants</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Chaos

ISSN

1054-1500

e-ISSN

1089-7682

Volume of the periodical

32

Issue of the periodical within the volume

5

Country of publishing house

US - UNITED STATES

Number of pages

17

Pages from-to

053111

UT code for WoS article

000827843300003

EID of the result in the Scopus database

2-s2.0-85129834719