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

Zhang, K; Hyvärinen, A

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

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Zhang, K
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://event.cwi.nl/uai2010/papers/UAI2010_0292.pdf
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Citation

Zhang, K., & Hyvärinen, 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.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-BF4A-6

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.