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Bayesian Factorial Linear Gaussian State-Space Models for Biosignal Decomposition

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2 Author(s)
Chiappa, S. ; IDIAP Res. Inst., Martigny ; Barber, D.

We discuss a method to extract independent dynamical systems underlying a single or multiple channels of observation. In particular, we search for one-dimensional subsignals to aid the interpretability of the decomposition. The method uses an approximate Bayesian analysis to determine automatically the number and appropriate complexity of the underlying dynamics, with a preference for the simplest solution. We apply this method to unfiltered EEG signals to discover low-complexity sources with preferential spectral properties, demonstrating improved interpretability of the extracted sources over related methods

Published in:

Signal Processing Letters, IEEE  (Volume:14 ,  Issue: 4 )

Date of Publication:

April 2007

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