Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
  Lokale TagsFreigabegeschichteDetailsÜbersicht

Freigegeben
Konferenzbeitrag

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

MPG-Autoren
/persons/resource/persons84328

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

Externe Ressourcen

https://event.cwi.nl/uai2010/papers/UAI2010_0292.pdf
(Verlagsversion)

Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PuRe verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

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.


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