Bayesian Correlated Component Analysis for inference of joint EEG activation

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

Bayesian Correlated Component Analysis for inference of joint EEG activation

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

City University of New York

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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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 language	English
Title of host publication	Proceedings of the 2014 International Workshop on Pattern Recognition in Neuroimaging
Number of pages	4
Publisher	IEEE
Publication date	2014
ISBN (Print)	978-1-4799-4149-0/14
Publication status	Published - 2014
Event	4th International Workshop on Pattern Recognition in Neuroimaging - Max Planck Institutes, Tübingen, Germany Duration: 4 Jun 2014 → 6 Jun 2014 Conference number: 4 http://mlin.kyb.tuebingen.mpg.de/prni2014/prni2014.html

Workshop

Workshop	4th International Workshop on Pattern Recognition in Neuroimaging
Number	4
Location	Max Planck Institutes
Country/Territory	Germany
City	Tübingen
Period	04/06/2014 → 06/06/2014
Internet address	http://mlin.kyb.tuebingen.mpg.de/prni2014/prni2014.html

Keywords

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

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@inproceedings{c0e7c2a90d8c4e66ab97942f00a66a94,

title = "Bayesian Correlated Component Analysis for inference of joint EEG activation",

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 = "Latent variable model, Multi-view , EEG, Variational inference, Canonical correlation analysis",

author = "Poulsen, \{Andreas Trier\} and Kamronn, \{Simon Due\} and Lucas Parra and Hansen, \{Lars Kai\}",

year = "2014",

language = "English",

isbn = "978-1-4799-4149-0/14",

booktitle = "Proceedings of the 2014 International Workshop on Pattern Recognition in Neuroimaging",

publisher = "IEEE",

address = "United States",

note = "4th International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014 ; Conference date: 04-06-2014 Through 06-06-2014",

url = "http://mlin.kyb.tuebingen.mpg.de/prni2014/prni2014.html",

}

TY - GEN

T1 - Bayesian Correlated Component Analysis for inference of joint EEG activation

AU - Poulsen, Andreas Trier

AU - Kamronn, Simon Due

AU - Parra, Lucas

AU - Hansen, Lars Kai

N1 - Conference code: 4

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - Latent variable model

KW - Multi-view

KW - EEG

KW - Variational inference

KW - Canonical correlation analysis

M3 - Article in proceedings

SN - 978-1-4799-4149-0/14

BT - Proceedings of the 2014 International Workshop on Pattern Recognition in Neuroimaging

PB - IEEE

T2 - 4th International Workshop on Pattern Recognition in Neuroimaging

Y2 - 4 June 2014 through 6 June 2014

ER -

Bayesian Correlated Component Analysis for inference of joint EEG activation

Abstract

Workshop

Keywords

OpenUrl availability

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Cite this