Brain electrical tomography using algebraic reconstruction techniques and Tikhonov regularization

dc.contributor.author	Βεντούρας, Ερρίκος Μ.	el
dc.contributor.author	Παπαγεωργίου, Χαράλαμπος	el
dc.contributor.author	Ραμπαβίλας, Ανδρέας Ν.	el
dc.contributor.author	Ουζούνογλου, Νικόλαος Κ.	el
dc.contributor.author	Στεφανής, Κωνσταντίνος Ν.	el
dc.date.accessioned	2015-02-02T08:54:16Z
dc.date.available	2015-02-02T08:54:16Z
dc.date.issued	2015-02-02
dc.identifier.uri	http://hdl.handle.net/11400/5427
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.subject	Algebra
dc.subject	Electroencephalography
dc.subject	Άλγεβρα
dc.subject	Ηλεκτροεγκεφαλογράφημα
dc.title	Brain electrical tomography using algebraic reconstruction techniques and Tikhonov regularization	en
heal.type	journalArticle
heal.generalDescription	Proceedings of the 22nd Annual International Conference of the IEEE	en
heal.classification	Medicine
heal.classification	Medical technology
heal.classification	Ιατρική
heal.classification	Ιατρικά όργανα και εξοπλισμός
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://skos.um.es/unescothes/C02465
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Ιατρικά όργανα και εξοπλισμός
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85003425
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85042138
heal.identifier.secondary	DOI: 10.1109/IEMBS.2000.901428
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2000
heal.bibliographicCitation	Ventouras, E., Papageorgiou, C., Rabavilas, A., Uzunoglu, N. and Stefanis, C. (2000). Brain electrical tomography using algebraic reconstruction techniques and Tikhonov regularization. Engineering in Medicine and Biology Society. vol. 4. pp. 2744-2747. IEEE Engineering in Medicine and Biology Society: Chicago, 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 noisy data perform similarly with TRT, but require the prior knowledge of the characteristic of the noise affecting the data. The empirical composite residual and smoothing operator (CRESO) criterion provides an approximation to the optimal regularization parameter t of the TRT, without requiring any prior knowledge about the noise in measured potentials. Therefore, when the CRESO criterion is successful in providing a t value. TRT may be used in real EEG data inversions for the creation of brain electrical activity tomographic images	en
heal.publisher	IEEE Engineering in Medicine and Biology Society	en
heal.journalName	Engineering in Medicine and Biology Society	en
heal.fullTextAvailability	true
heal.conferenceItemType	full paper