Estimation of time-varying causal connectivity on EEG signals with the use of adaptive autoregressive parameters

Giannakakis, GA; Nikita, KS

dc.contributor.author	Giannakakis, GA	en
dc.contributor.author	Nikita, KS	en
dc.date.accessioned	2014-03-01T02:45:15Z
dc.date.available	2014-03-01T02:45:15Z
dc.date.issued	2008	en
dc.identifier.issn	1557170X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32240
dc.subject	Causal Relation	en
dc.subject	Efficient Estimation	en
dc.subject	event-related potential erp	en
dc.subject	Flow Pattern	en
dc.subject	Information Flow	en
dc.subject	Temporal Dynamics	en
dc.subject	Directed Transfer Function	en
dc.subject	kalman filter	en
dc.subject	Time Dependent	en
dc.subject	Time Varying	en
dc.subject.other	Control theory	en
dc.subject.other	Electroencephalography	en
dc.subject.other	Flow patterns	en
dc.subject.other	Patient monitoring	en
dc.subject.other	Adaptive autoregressive	en
dc.subject.other	Brain areas	en
dc.subject.other	Causal relations	en
dc.subject.other	Connectivity patterns	en
dc.subject.other	Direct flows	en
dc.subject.other	Directed transfer functions	en
dc.subject.other	EEG signals	en
dc.subject.other	Event related-potentials	en
dc.subject.other	Information flows	en
dc.subject.other	Multivariate autoregressive	en
dc.subject.other	Non stationaries	en
dc.subject.other	Physiological signals	en
dc.subject.other	Simulated signals	en
dc.subject.other	Temporal dynamics	en
dc.subject.other	Time-dependent flows	en
dc.subject.other	Time-varying	en
dc.subject.other	Time varying systems	en
dc.title	Estimation of time-varying causal connectivity on EEG signals with the use of adaptive autoregressive parameters	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IEMBS.2008.4649963	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IEMBS.2008.4649963	en
heal.identifier.secondary	4649963	en
heal.publicationDate	2008	en
heal.abstract	In this paper, we address the problem of time-varying causal connectivity estimators on Electro-encephalographic (EEG) signals by means of Directed Transfer Function (DTF). The DTF method reveals causal information flows between brain areas, while direct DTF (dDTF) is able to distinguish and estimate only direct flows. Since neuro-physiological signals such as EEG and event related potentials (ERP) can be nonstationary, their temporal dynamics cannot be satisfactorily represented. Time-varying dDTF can be estimated using Kalman Filter for adaptive calculation of multivariate autoregressive coefficients. This approach can reveal transient causal relations and model time-dependent flow patterns. This approach was applied to simulated signals and the results indicated that time-varying dDTF can provide efficient estimates of connectivity patterns. © 2008 IEEE.	en
heal.journalName	Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - ""Personalized Healthcare through Technology""	en
dc.identifier.doi	10.1109/IEMBS.2008.4649963	en
dc.identifier.volume	2008	en
dc.identifier.spage	3512	en
dc.identifier.epage	3515	en