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A Multivariate Method for Dynamic System Analysis: Multivariate Detrended Fluctuation Analysis Using Generalized
Sebastian Wallot1,2, Julien Patrick Irmer3, Monika Tschense1,4
1Institute for Sustainability Education and Psychology, Leuphana University of Lüneburg.
Topics in Cognitive Science
|September 14, 2023
Summary
We introduce a new method, multivariate detrended fluctuation analysis (mvDFA), to analyze fractal fluctuations in multiple interacting brain signals. This approach enhances understanding of dynamic systems in human behavior and cognition.
Area of Science:
- Cognitive Science
- Neuroscience
- Complex Systems
Background:
- Fractal fluctuations are key to understanding human behavior and cognition through dynamic systems theory.
- Existing methods often overlook interdependencies between multiple time series.
Purpose of the Study:
- To introduce a generalized variance method for multivariate detrended fluctuation analysis (mvDFA).
- To enable the analysis of fractal properties in multivariate time series, accounting for intercorrelations.
Main Methods:
- Description of the generalized variance method for mvDFA.
- Application to simulated data to demonstrate advantages.
- Investigation of empirical electroencephalographic (EEG) data during a time-estimation task.
Main Results:
- mvDFA successfully analyzes fractal fluctuations in multivariate time series.
- The method accounts for intercorrelations between time series.
- Demonstrated application on EEG data reveals insights into cognitive processes.
Conclusions:
- mvDFA is a valuable methodological development for dynamic systems research in human behavior.
- Multivariate analysis advances theoretical understanding of interaction-dominant dynamics in cognition.
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