Bayesian Factorial Linear Gaussian State-Space Models for Biosignal Decomposition

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:
IEEE Signal Processing Letters
Year:
2007
Note:
IDIAP-RR 05-84
Laboratories:




 Record created 2010-02-11, last modified 2018-03-17

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