Enhancing dominant modes in nonstationary time series by means of the symbolic resonance analysis.

Enhancing dominant modes in nonstationary time series by means of the symbolic resonance analysis. Research Abstract Details 

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Enhancing dominant modes in nonstationary time series by means of the symbolic resonance analysis. Abstract Text:

Peter Beim Graben,Heiner Drenhaus,Eva Brehm,Bela Rhode,Douglas Saddy,Stefan Frisch,Peter beim Graben,Heiner Drenhaus,Eva Brehm,Bela Rhode,Douglas Saddy,Stefan Frisch,Peter beim Graben,Heiner Drenhaus,Eva Brehm,Bela Rhode,Douglas Saddy,Stefan Frisch,

We present the symbolic resonance analysis (SRA) as a viable method for addressing the problem of enhancing a weakly dominant mode in a mixture of impulse responses obtained from a nonlinear dynamical system. We demonstrate this using results from a numerical simulation with Duffing oscillators in different domains of their parameter space, and by analyzing event-related brain potentials (ERPs) from a language processing experiment in German as a representative application. In this paradigm, the averaged ERPs exhibit an N400 followed by a sentence final negativity. Contemporary sentence processing models predict a late positivity (P600) as well. We show that the SRA is able to unveil the P600 evoked by the critical stimuli as a weakly dominant mode from the covering sentence final negativity.

Enhancing dominant modes in nonstationary time series by means of the symbolic resonance analysis. Publishing Authors By Initials

P Beim Graben,H Drenhaus,E Brehm,B Rhode,D Saddy,S Frisch,P beim Graben,H Drenhaus,E Brehm,B Rhode,D Saddy,S Frisch,P beim Graben,H Drenhaus,E Brehm,B Rhode,D Saddy,S Frisch,

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Enhancing dominant modes in nonstationary time series by means of the symbolic resonance analysis. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: Chaos (Woodbury, N.Y.)

VOLUME: 17

Page Numbers: 043106

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ISSN: 1054-1500

DAY: 31

MONTH: Dec

YEAR: 2007

Enhancing dominant modes in nonstationary time series by means of the symbolic resonance analysis. Information

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LANGUAGE: eng

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Grant and Affiliation Information for Enhancing dominant modes in nonstationary time series by means of the symbolic resonance analysis.

AFFILIATION: School of Psychology and Clinical Language Sciences, University of Reading, Reading RG6 6AH, United Kingdom.

Country: United States

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MEDLINETA: Chaos

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