Source separation and higher-order causal analysis of MEG and EEG

Kun Zhang, Aapo Hyvärinen
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:734-741, 2010.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR8-zhang10e, title = {Source separation and higher-order causal analysis of {MEG} and {EEG}}, author = {Zhang, Kun and Hyv{\"a}rinen, Aapo}, booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence}, pages = {734--741}, year = {2010}, editor = {Grünwald, Peter and Spirtes, Peter}, volume = {R8}, series = {Proceedings of Machine Learning Research}, month = {08--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r8/main/assets/zhang10e/zhang10e.pdf}, url = {https://proceedings.mlr.press/r8/zhang10e.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Source separation and higher-order causal analysis of MEG and EEG %A Kun Zhang %A Aapo Hyvärinen %B Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2010 %E Peter Grünwald %E Peter Spirtes %F pmlr-vR8-zhang10e %I PMLR %P 734--741 %U https://proceedings.mlr.press/r8/zhang10e.html %V R8 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Zhang, K. & Hyvärinen, A.. (2010). Source separation and higher-order causal analysis of MEG and EEG. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R8:734-741 Available from https://proceedings.mlr.press/r8/zhang10e.html. Reissued by PMLR on 04 October 2026.

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