%Aigaion2 BibTeX export from Idiap Publications
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@ARTICLE{silviachiappa:ieee_spl:2007,
         author = {Chiappa, Silvia and Barber, David},
       projects = {Idiap},
          title = {Bayesian Factorial Linear Gaussian State-Space Models for Biosignal Decomposition},
        journal = {{IEEE} Signal Processing Letters},
           year = {2007},
           note = {IDIAP-RR 05-84},
       crossref = {silviachiappa:rr05-84},
       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.},
            pdf = {https://publications.idiap.ch/attachments/papers/2007/silviachiappa-ieee_spl-2007.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/papers/2007/silviachiappa-ieee_spl-2007.ps.gz},
ipdmembership={learning},
}



crossreferenced publications: 
@TECHREPORT{silviachiappa:rr05-84,
         author = {Chiappa, Silvia and Barber, David},
       projects = {Idiap},
          title = {Bayesian Factorial Linear Gaussian State-Space Models for Biosignal Decomposition},
           type = {Idiap-RR},
         number = {Idiap-RR-84-2005},
           year = {2005},
    institution = {IDIAP},
       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.},
            pdf = {https://publications.idiap.ch/attachments/reports/2005/silviachiappa-idiap-rr-05-84.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2005/silviachiappa-idiap-rr-05-84.ps.gz},
ipdmembership={learning},
}