Title :
Bayesian correlated component analysis for inference of joint EEG activation
Author :
Poulsen, Andreas Trier ; Kamronn, Simon ; Parra, L.C. ; Hansen, Lars Kai
Author_Institution :
Dept. of Appl. Math. & Comput. Sci., Tech. Univ. of Denmark, Lyngby, Denmark
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.
Keywords :
Bayes methods; electroencephalography; medical signal processing; Bayesian correlated component analysis; human information processing; joint EEG activation inference; probabilistic generative multiview model; representational universality; simulated data; well-established benchmark EEG dataset; Bayes methods; Brain modeling; Correlation; Electroencephalography; Probabilistic logic; Signal to noise ratio; Standards; EEG; Latent variable model; canonical correlation analysis; multi-view; variational in-ference;
Conference_Titel :
Pattern Recognition in Neuroimaging, 2014 International Workshop on
Conference_Location :
Tubingen
Print_ISBN :
978-1-4799-4150-6
DOI :
10.1109/PRNI.2014.6858539
