Tikhonov regularization techniques in simulated brain electrical tomography

Ventouras, E; Papageorgiou, C; Uzunoglu, N; Koulouridis, S; Rabavilas, A; Stefanis, C

dc.contributor.author	Ventouras, E	en
dc.contributor.author	Papageorgiou, C	en
dc.contributor.author	Uzunoglu, N	en
dc.contributor.author	Koulouridis, S	en
dc.contributor.author	Rabavilas, A	en
dc.contributor.author	Stefanis, C	en
dc.date.accessioned	2014-03-01T01:50:32Z
dc.date.available	2014-03-01T01:50:32Z
dc.date.issued	2000	en
dc.identifier.issn	0205-2067	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/26043
dc.subject.classification	Biotechnology & Applied Microbiology	en
dc.subject.other	POTENTIALS	en
dc.title	Tikhonov regularization techniques in simulated brain electrical tomography	en
heal.type	journalArticle	en
heal.language	English	en
heal.publicationDate	2000	en
heal.abstract	In order to solve the inverse electroencephalographic (EEG) problem, the unknown intracranial current sources must be calculated. The solution provides a tomographical representation of the brain activity. In the present work the quality of the solutions given by the optimized Tikhonov Regularization Technique (TRT) is assessed, in comparison to the Algebraic Reconstruction Techniques (ART). Results show that ART algorithms specifically designed to compensate for noisy data perform similarly with TRT, but require the prior knowledge of the characteristic of the noise affecting the data. This is not a prerequisite for the TRT method. Therefore, when TRT optimization criteria are successful, this methods may be used in real EEG data inversions providing tomographic solutions in a wider context than ART.	en
heal.publisher	DIAGNOSIS PRESS LTD	en
heal.journalName	BIOTECHNOLOGY & BIOTECHNOLOGICAL EQUIPMENT	en
dc.identifier.isi	ISI:000089131500018	en
dc.identifier.volume	14	en
dc.identifier.issue	1	en
dc.identifier.spage	95	en
dc.identifier.epage	99	en