Bayesian Correlated Component Analysis for inference of joint EEG activation

Andreas Trier Poulsen, Simon Due Kamronn, Lucas Parra, Lars Kai Hansen

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Abstract

We propose a probabilistic generative multi-view model to test the representational universality of human information processing. The model is tested in simulated data and in a well-established benchmark EEG dataset.
Original languageEnglish
Title of host publicationProceedings of the 2014 International Workshop on Pattern Recognition in Neuroimaging
Number of pages4
PublisherIEEE
Publication date2014
ISBN (Print)978-1-4799-4149-0/14
Publication statusPublished - 2014
Event4th International Workshop on Pattern Recognition in Neuroimaging - Max Planck Institutes, Tübingen, Germany
Duration: 4 Jun 20146 Jun 2014
Conference number: 4
http://mlin.kyb.tuebingen.mpg.de/prni2014/prni2014.html

Workshop

Workshop4th International Workshop on Pattern Recognition in Neuroimaging
Number4
LocationMax Planck Institutes
CountryGermany
CityTübingen
Period04/06/201406/06/2014
Internet address

Keywords

  • Latent variable model
  • Multi-view
  • EEG
  • Variational inference
  • Canonical correlation analysis

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