Brain electrical tomography using algebraic reconstruction techniques and Tikhonov regularization

Ventouras, E; Uzunoglu, N; Papageorgiou, C; Rabailas, A; Kechribaris, C; Stefanis, C

dc.contributor.author	Ventouras, E	en
dc.contributor.author	Uzunoglu, N	en
dc.contributor.author	Papageorgiou, C	en
dc.contributor.author	Rabailas, A	en
dc.contributor.author	Kechribaris, C	en
dc.contributor.author	Stefanis, C	en
dc.date.accessioned	2014-03-01T02:48:56Z
dc.date.available	2014-03-01T02:48:56Z
dc.date.issued	2000	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34144
dc.subject	Algebraic Reconstruction Technique	en
dc.subject	Is Success	en
dc.subject	Noisy Data	en
dc.subject	Performance Evaluation	en
dc.subject	Performing Art	en
dc.subject	Prior Knowledge	en
dc.subject	tikhonov regularization	en
dc.title	Brain electrical tomography using algebraic reconstruction techniques and Tikhonov regularization	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IEMBS.2000.901428	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IEMBS.2000.901428	en
heal.publicationDate	2000	en
heal.abstract	The inverse EEG problem is solved using simulated potentials, through an analytic technique providing information about extended intracranial distributions, with separate source and sink positions. A three-layered concentric sphere model is used for representing head geometry. Comparative performance evaluation of the Algebraic Reconstruction Techniques (ART) and the Tikhonov Regularization Technique (TRT) is performed. ART algorithms specifically designed to compensate for	en
heal.journalName	Annual International Conference of the IEEE Engineering in Medicine and Biology Society	en
dc.identifier.doi	10.1109/IEMBS.2000.901428	en