Fitting of one ARMA model to multiple trials increases the time resolution of instantaneous coherence

This study presents a least mean squares (LMS) algorithm for the ensemble modeling of a multivariate ARMA process. Generally, an LMS algorithm makes possible the tracking of parameters for nonstationary time series. Our estimation incorporates multiple process observations that improve the accuracy...

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Bibliographic Details
Published in:Biological cybernetics 2003-10, Vol.89 (4), p.303-312
Main Authors: Möller, Eva, Schack, Bärbel, Vath, Nuria, Witte, Herbert
Format: Article
Language:English
Subjects:
Algorithms
Electroencephalography - methods
Humans
Least-Squares Analysis
Memory - physiology
Models, Neurological
Parameter estimation
Studies
Time Factors
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Summary:This study presents a least mean squares (LMS) algorithm for the ensemble modeling of a multivariate ARMA process. Generally, an LMS algorithm makes possible the tracking of parameters for nonstationary time series. Our estimation incorporates multiple process observations that improve the accuracy of the parameter estimation. As a consequence, the estimation sequences come close to the true model parameters with a fast adaptation speed. This advantage also holds true of spectral quantities (e.g., the momentary coherence), which are derived from the model parameters. Thus the extension of the ARMA fitting from one to multiple trajectories allows the investigation of nonstationary biological signals with an increased time resolution. The applicability of the algorithm is demonstrated for event-related EEG coherence analysis of the Sternberg task. The changing interaction between posterior association cortex and anterior brain area was shown for verbal and nonverbal stimuli by means of the time-variant theta coherence.
ISSN:0340-1200
1432-0770
DOI:10.1007/s00422-003-0394-x