ARTICLE silviachiappa:ieee_spl:2007/IDIAP Bayesian Factorial Linear Gaussian State-Space Models for Biosignal Decomposition Chiappa, Silvia Barber, David EXTERNAL http://publications.idiap.ch/attachments/papers/2007/silviachiappa-ieee_spl-2007.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/silviachiappa:rr05-84 Related documents IEEE Signal Processing Letters 2007 IDIAP-RR 05-84 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. REPORT silviachiappa:rr05-84/IDIAP Bayesian Factorial Linear Gaussian State-Space Models for Biosignal Decomposition Chiappa, Silvia Barber, David EXTERNAL http://publications.idiap.ch/attachments/reports/2005/silviachiappa-idiap-rr-05-84.pdf PUBLIC Idiap-RR-84-2005 2005 IDIAP 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.