Source Separation and Higher-Order Causal Analysis of MEG and EEG
This work addresses source separation and connectivity analysis for EEG/MEG researchers, offering an incremental improvement with a novel method for known bottlenecks.
The paper tackles the problem of automatically separating sources and analyzing their connectivity in EEG/MEG data by proposing a two-layer model that identifies conditionally uncorrelated sources with causal dependencies in their time-varying variances, resulting in a causal diagram that groups functionally similar sources with negative influences between groups in MEG experiments.
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.