English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Source Separation and Higher-Order Causal Analysis of MEG and EEG

Zhang, K., & Hyv ̈arinen, A. (2010). Source Separation and Higher-Order Causal Analysis of MEG and EEG. In P. Grünwald, & P. Spirtes (Eds.), 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010) (pp. 709-716). Corvallis, OR, USA: AUAI Press.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BF4A-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-81CC-6
Genre: Conference Paper

Files

show Files

Locators

show
hide
Description:
-

Creators

show
hide
 Creators:
Zhang, K1, 2, Author              
Hyv ̈arinen, A, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: Separation of the sources and analysis of their connectivity have been an important topic in EEG/MEG analysis. To solve this problem in an automatic manner, we propose a twolayer model, in which the sources are conditionally uncorrelated from each other, but not independent; the dependence is caused by the causality in their time-varying variances (envelopes). The model is identified in two steps. We first propose a new source separation technique which takes into account the autocorrelations (which may be time-varying) and time-varying variances of the sources. The causality in the envelopes is then discovered by exploiting a special kind of multivariate GARCH (generalized autoregressive conditional heteroscedasticity) model. The resulting causal diagram gives the effective connectivity between the separated sources; in our experimental results on MEG data, sources with similar functions are grouped together, with negative influences between groups, and the groups are connected via some interesting sources.

Details

show
hide
Language(s):
 Dates: 2010-07
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: 6630
 Degree: -

Event

show
hide
Title: 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)
Place of Event: Catalina Island, CA, USA
Start-/End Date: 2010-07-08 - 2010-07-11

Legal Case

show

Project information

show

Source 1

show
hide
Title: 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)
Source Genre: Proceedings
 Creator(s):
Grünwald, P, Editor
Spirtes, P, Editor
Affiliations:
-
Publ. Info: Corvallis, OR, USA : AUAI Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 709 - 716 Identifier: ISBN: 978-0-9749039-6-5