An artificial neural network approach to the classification of inferred intracranial signals

VASIOS, C; MATSOPOULOS, G

dc.contributor.author	VASIOS, C	en
dc.contributor.author	MATSOPOULOS, G	en
dc.date.accessioned	2014-03-01T01:54:50Z
dc.date.available	2014-03-01T01:54:50Z
dc.date.issued	2006	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/27494
dc.relation.uri	http://www.wseas.us/e-library/conferences/2006istanbul/papers/521-187.pdf	en
dc.subject	Algebraic Reconstruction Technique	en
dc.subject	Artificial Neural Network	en
dc.subject	Autoregressive Model	en
dc.subject	Back Propagation Algorithm	en
dc.subject	Brain Mapping	en
dc.subject	Cognitive Science	en
dc.subject	Comparative Analysis	en
dc.subject	event-related potential erp	en
dc.subject	Feature Extraction	en
dc.subject	Inverse Method	en
dc.subject	Simulated Annealing	en
dc.subject	Leave One Out Cross Validation	en
dc.subject	Low Resolution	en
dc.subject	Multi Layer Perceptron	en
dc.subject	Normal Control	en
dc.subject	Neural Network	en
dc.title	An artificial neural network approach to the classification of inferred intracranial signals	en
heal.type	journalArticle	en
heal.publicationDate	2006	en
heal.abstract	Event-Related Potentials (ERPs) provide non invasive measurements of the electrical activity on the scalp that are linked to the presentation of stimuli and events. Brain mapping techniques are able to provide evidence on the solution of debatable issues in cognitive science. In this paper, an effective signal classification approach is proposed, extending the use of two inversion techniques: the Brain	en