Uncertainty in Artificial Intelligence
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Source Separation and Higher-Order Causal Analysis of MEG and EEG
Kun Zhang, Aapo Hyvarinen
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 two-layer 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.
Keywords:
Pages: 709-716
PS Link:
PDF Link: /papers/10/p709-zhang.pdf
BibTex:
@INPROCEEDINGS{Zhang10,
AUTHOR = "Kun Zhang and Aapo Hyvarinen",
TITLE = "Source Separation and Higher-Order Causal Analysis of MEG and EEG",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "709--716"
}


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