Classification of event related potentials of error- related observations using support vector machines

dc.contributor.author	Ασβεστάς, Παντελής	el
dc.contributor.author	Βεντούρας, Ερρίκος	el
dc.contributor.author	Καρανάσιου, Ειρήνη	el
dc.contributor.author	Ματσόπουλος, Γιώργος	el
dc.date.accessioned	2015-06-06T14:10:06Z
dc.date.available	2015-06-06T14:10:06Z
dc.date.issued	2015-06-06
dc.identifier.uri	http://hdl.handle.net/11400/15316
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.source	http://link.springer.com	en
dc.subject	Observational-learning mechanisms
dc.subject	Support vector machines
dc.subject	Μηχανισμοί μάθησης μέσω παρακολούθησης
dc.subject	Μηχανές υποστήριξης διανυσμάτων
dc.title	Classification of event related potentials of error- related observations using support vector machines	el
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Medical physics
heal.classification	Ιατρική
heal.classification	Ιατρική φυσική
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85083001
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Ιατρική φυσική
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2008009003
heal.identifier.secondary	DOI: 10.1007/978-3-642-41016-1_5
heal.language	en
heal.access	campus
heal.publicationDate	2013
heal.bibliographicCitation	Asvestas, P., Ventouras, E., Karanasiou, I. and Matsopoulos, G. (2013) Classification of event related potentials of error- related observations using support vector machines. "Communications in Computer and Information Science", 384, p.40-49	en
heal.abstract	The aim of this paper is to present a classification method that is capable to discriminate between Event Related Potentials (ERPs) that are the result of observation of correct and incorrect actions. ERP data from 47 electrodes were acquired from eight volunteers (observers), who observed correct or incorrect responses of subjects (actors) performing a special designed task. A number of histogram-related features were calculated from each ERP recording and the most significant ones were selected using a statistical ranking criterion. The Support Vector Machines algorithm combined with the leave-one-out technique was used for the classification task. The proposed approach discriminated the two classes (observation of correct and incorrect actions) with accuracy 100%. The proposed ERP-signal classification method provides a promising tool to study observational-learning mechanisms in joint-action research and may foster the future development of systems capable of automatically detecting erroneous actions in human-human and human-artificial agent interactions.	en
heal.publisher	Springer	el
heal.journalName	Communications in Computer and Information Science	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true