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Titolo:
EEC source localization: A neural network approach
Autore:
Sclabassi, RJ; Sonmez, M; Sun, MH;
Indirizzi:
Univ Pittsburgh, Dept Neurol Surg, Lab Computat Neurosci, Pittsburgh, PA 15260 USA Univ Pittsburgh Pittsburgh PA USA 15260 eurosci, Pittsburgh, PA 15260 USA Univ Pittsburgh, Dept Elect Engn, Lab Computat Neurosci, Pittsburgh, PA 15260 USA Univ Pittsburgh Pittsburgh PA USA 15260 eurosci, Pittsburgh, PA 15260 USA Univ Pittsburgh, Dept Bioengn, Lab Computat Neurosci, Pittsburgh, PA 15260USA Univ Pittsburgh Pittsburgh PA USA 15260 Neurosci, Pittsburgh, PA 15260USA
Titolo Testata:
NEUROLOGICAL RESEARCH
fascicolo: 5, volume: 23, anno: 2001,
pagine: 457 - 464
SICI:
0161-6412(200107)23:5<457:ESLANN>2.0.ZU;2-F
Fonte:
ISI
Lingua:
ENG
Soggetto:
VOLUME CONDUCTOR; MODELS; BRAIN; HEAD;
Keywords:
source localization; artificial neural networks; hierarchical decision making; forward problem; inverse problem;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Citazioni:
27
Recensione:
Indirizzi per estratti:
Indirizzo: Sclabassi, RJ Presbyterian Univ Hosp, Dept Neurol Surg, Suite B-400,200 Lothrop St, Pittsburgh, PA 15213 USA Presbyterian Univ Hosp Suite B-400,200 Lothrop St Pittsburgh PA USA 15213
Citazione:
R.J. Sclabassi et al., "EEC source localization: A neural network approach", NEUROL RES, 23(5), 2001, pp. 457-464

Abstract

Functional activity in the brain is associated with the generation of currents and resultant voltages which may be observed on the scalp as the electroencephelogram. The current sources may be modeled as dipoles. The properties of the current dipole sources may be studied by solving either the forward or in verse problems. The forward problem utilizes a volume conductor model for the head, in which the potentials on the conductor surface are computed based on an assumed current dipole at an arbitrary location, orientation, and strength. In the inverse problem, on the other hand, a current dipole, or a group of dipoles, is identified based on the observed EEG. Both the forward and inverse problems are typically solved by numerical procedures, such as a boundary element method and an optimization algorithm. These approaches are highly time-consuming and unsuitable for the rapid evaluationof brain function. in this paper we present a different approach to these problems based on machine learning. We solve both problems using artificialneural networks which are trained off-line using back-propagation techniques to learn the complex source-potential relationships of head volume conduction. Once trained, these networks are able to generalize their knowledge to localize functional activity within the brain in a computationally efficient manner.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 29/03/20 alle ore 14:55:54