@inproceedings{6630,
  title = {Source Separation and Higher-Order Causal Analysis of MEG and EEG},
  journal = {Uncertainty in Artificial Intelligence: Proceedings of the Twenty-Sixth Conference (UAI 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 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.},
  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},
  slug = {6630},
  author = {Zhang, K. and Hyv{\"a}rinen, A.},
  month_numeric = {7}
}