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

2010

Conference Paper

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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.

Author(s): Zhang, K. and Hyvärinen, A.
Journal: Uncertainty in Artificial Intelligence: Proceedings of the Twenty-Sixth Conference (UAI 2010)
Pages: 709-716
Year: 2010
Month: July
Day: 0
Editors: Gr{\"u}nwald, P. , P. Spirtes
Publisher: AUAI Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Address: Corvallis, OR, USA
Digital: 0
Event Name: 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)
Event Place: Catalina Island, CA, USA
ISBN: 978-0-9749039-6-5
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{6630,
  title = {Source Separation and Higher-Order Causal Analysis of MEG and EEG},
  author = {Zhang, K. and Hyv{\"a}rinen, A.},
  journal = {Uncertainty in Artificial Intelligence: Proceedings of the Twenty-Sixth Conference (UAI 2010)},
  pages = {709-716},
  editors = {Gr{\"u}nwald, P. , P. Spirtes},
  publisher = {AUAI Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Corvallis, OR, USA},
  month = jul,
  year = {2010},
  month_numeric = {7}
}