PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bayesian Factorial Linear Gaussian State-Space Models for Biosignal Decomposition
Silvia Chiappa and David Barber
Signal Processing Letters 2007.

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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
Theory & Algorithms
ID Code:2229
Deposited By:Silvia Chiappa
Deposited On:06 October 2006