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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GF21-14727K" target="_blank" >GF21-14727K: Struktury synchronizace v mnohorozměrných neurálních signálech: strojové učení a predikce účinnosti antidepresiv</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Chaos

ISSN

1054-1500

e-ISSN

1089-7682

Svazek periodika

32

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

17

Strana od-do

053111

Kód UT WoS článku

000827843300003

EID výsledku v databázi Scopus

2-s2.0-85129834719