DocumentCode :
1231895
Title :
Bayesian Factorial Linear Gaussian State-Space Models for Biosignal Decomposition
Author :
Chiappa, Silvia ; Barber, David
Author_Institution :
IDIAP Res. Inst., Martigny
Volume :
14
Issue :
4
fYear :
2007
fDate :
4/1/2007 12:00:00 AM
Firstpage :
267
Lastpage :
270
Abstract :
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
Keywords :
Bayes methods; Gaussian processes; electroencephalography; medical signal processing; state-space methods; Bayesian factorial linear model; Gaussian state-space model; biosignal decomposition; electroencephalography; independent dynamical system; low-complexity sources; multiple channels; one-dimensional subsignals; preferential spectral properties; unfiltered EEG signals; Bayesian methods; Biomedical signal processing; Brain modeling; Electroencephalography; Filtering; Frequency; Independent component analysis; Kalman filters; Smoothing methods; Vectors; EEG; independent dynamical processes; linear Gaussian state–space model; unified inference; variational Bayes;
fLanguage :
English
Journal_Title :
Signal Processing Letters, IEEE
Publisher :
ieee
ISSN :
1070-9908
Type :
jour
DOI :
10.1109/LSP.2006.881515
Filename :
4130390
Link To Document :
بازگشت