Determinism Testing of Low-Dimensional Signals Embedded in High-Dimensional Multivariate Time Series

Frühauf, C.; Hartmann, Steffen; Seifert, Bastian; Uhl, Christian

doi:10.1007/978-3-030-35441-1_1

Cited by 1 publication

(2 citation statements)

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“…Dynamical Component Analysis (DyCA), a method to decompose a multivariate signal into time-dependent amplitudes and corresponding multivariate modes (40), was presented. Thereby the amplitudes are estimated with the goal of optimally obeying a set of coupled ODEs.…”

Section: Discussionmentioning

confidence: 99%

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Subspace Detection and Blind Source Separation of Multivariate Signals by Dynamical Component Analysis (DyCA)

Uhl

Kern

Warmuth

et al. 2020

IEEE Open J. Signal Process.

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The decomposition of a multivariate signal is an important tool for the analysis of measured or simulated data leading to possible detection of the relevant subspace or the sources of the signal. A new method -dynamical component analysis (DyCA) -is based on modeling the signal by a set of coupled ordinary differential equations. Its derivation and its features are presented in-depth. The corresponding algorithm is nearly as simple as principal component analysis (PCA). The results obtained by DyCA however yield a deeper insight into the underlying dynamics of the data. To illustrate the broad area of possible applications a set of examples of analyzing data by DyCA is presented -involving both measured EEG, motion and ECG data as well as data obtained from stochastic differential equations. Thereby our alternative tool for dimensionality reduction is compared toresults obtained PCA and ICA and demonstrate the gain of this approach.

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Section: Discussionmentioning

confidence: 99%

“…Another example of the application of DyCA on EEG data is given in [40]. Different approaches are investigated to test multivariate EEG data for determinism by the Kaplan-Glass determinism test [41].…”

Section: A Iceeg-datamentioning

confidence: 99%