A Multivariate Method for Dynamic System Analysis: Multivariate Detrended Fluctuation Analysis Using Generalized Variance
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In: Topics in Cognitive Science, 14.09.2023.
Research output: Journal contributions › Journal articles › Research › peer-review
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TY - JOUR
T1 - A Multivariate Method for Dynamic System Analysis
T2 - Multivariate Detrended Fluctuation Analysis Using Generalized Variance
AU - Wallot, Sebastian
AU - Irmer, Julien Patrick
AU - Tschense, Monika
AU - Kuznetsov, Nikita
AU - Højlund, Andreas
AU - Dietz, Martin
N1 - Publisher Copyright: © 2023 The Authors. Topics in Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society.
PY - 2023/9/14
Y1 - 2023/9/14
N2 - Fractal fluctuations are a core concept for inquiries into human behavior and cognition from a dynamic systems perspective. Here, we present a generalized variance method for multivariate detrended fluctuation analysis (mvDFA). The advantage of this extension is that it can be applied to multivariate time series and considers intercorrelation between these time series when estimating fractal properties. First, we briefly describe how fractal fluctuations have advanced a dynamic system understanding of cognition. Then, we describe mvDFA in detail and highlight some of the advantages of the approach for simulated data. Furthermore, we show how mvDFA can be used to investigate empirical multivariate data using electroencephalographic recordings during a time-estimation task. We discuss this methodological development within the framework of interaction-dominant dynamics. Moreover, we outline how the availability of multivariate analyses can inform theoretical developments in the area of dynamic systems in human behavior.
AB - Fractal fluctuations are a core concept for inquiries into human behavior and cognition from a dynamic systems perspective. Here, we present a generalized variance method for multivariate detrended fluctuation analysis (mvDFA). The advantage of this extension is that it can be applied to multivariate time series and considers intercorrelation between these time series when estimating fractal properties. First, we briefly describe how fractal fluctuations have advanced a dynamic system understanding of cognition. Then, we describe mvDFA in detail and highlight some of the advantages of the approach for simulated data. Furthermore, we show how mvDFA can be used to investigate empirical multivariate data using electroencephalographic recordings during a time-estimation task. We discuss this methodological development within the framework of interaction-dominant dynamics. Moreover, we outline how the availability of multivariate analyses can inform theoretical developments in the area of dynamic systems in human behavior.
KW - Detrended fluctuation analysis
KW - Dynamic systems
KW - Interaction-dominant dynamics
KW - Multivariate analysis
KW - R package
KW - Time estimation
KW - Psychology
UR - http://www.scopus.com/inward/record.url?scp=85170689774&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/0fcb8926-34ff-3ce6-a10f-e20bedaaf31f/
U2 - 10.1111/tops.12688
DO - 10.1111/tops.12688
M3 - Journal articles
C2 - 37706618
AN - SCOPUS:85170689774
JO - Topics in Cognitive Science
JF - Topics in Cognitive Science
SN - 1756-8757
ER -