Bayesian correlated component analysis for inference of joint EEG activation

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.

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Bibliographic Details
Published in2014 International Workshop on Pattern Recognition in Neuroimaging pp. 1 - 4
Main Authors Poulsen, Andreas Trier, Kamronn, Simon, Parra, Lucas C., Hansen, Lars Kai
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2014
Subjects
Bayes methods
Brain modeling
canonical correlation analysis
Correlation
EEG
Electroencephalography
Latent variable model
multi-view
Probabilistic logic
Signal to noise ratio
variational in-ference
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Summary: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.
DOI:10.1109/PRNI.2014.6858539