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

0558251 - ÚI 2023 RIV US eng J - Článek v odborném periodiku
Vlachos, Ioannis - Kugiumtzis, D. - Paluš, Milan
Phase-Based Causality Analysis with Partial Mutual Information from Mixed Embedding.
Chaos. Roč. 32, č. 5 (2022), č. článku 053111. ISSN 1054-1500. E-ISSN 1089-7682
Grant CEP: GA ČR(CZ) GF21-14727K
Grant ostatní: AV ČR(CZ) AP1901
Program: Akademická prémie - Praemium Academiae
Institucionální podpora: RVO:67985807
Klíčová slova: causality * phase dynamics * synchronization * EEG
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impakt faktor: 2.9, rok: 2022
Způsob publikování: Omezený přístup
http://dx.doi.org/10.1063/5.0087910

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.
Trvalý link: http://hdl.handle.net/11104/0331979