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Bayesian factorial linear Gaussian state-space models for biosignal decomposition

Chiappa, S; Barber, D; (2007) Bayesian factorial linear Gaussian state-space models for biosignal decomposition. IEEE Signal Processing Letters , 14 (4) pp. 267-270. 10.1109/LSP.2006.881515. Green open access

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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. © 2007 IEEE.

Type: Article
Title: Bayesian factorial linear Gaussian state-space models for biosignal decomposition
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/LSP.2006.881515
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/19781
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