Classification of error-related negativity (ERN) and positivity (PE) potentials using kNN and support vector machines

dc.contributor.author	Βεντούρας, Ερρίκος Μ.	el
dc.contributor.author	Ασβεστάς, Παντελής Α.	el
dc.contributor.author	Καρανάσιου, Ειρήνη Σ.	el
dc.contributor.author	Ματσόπουλος, Γεώργιος Κ.	el
dc.date.accessioned	2015-02-06T10:23:54Z
dc.date.available	2015-02-06T10:23:54Z
dc.date.issued	2015-02-06
dc.identifier.uri	http://hdl.handle.net/11400/5731
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://www.elsevier.com	en
dc.subject	Support vector machines
dc.subject	Error positivity
dc.subject	Μηχανές διανυσμάτων υποστήριξης
dc.subject	Θετικότητα σφάλματος
dc.title	Classification of error-related negativity (ERN) and positivity (PE) potentials using kNN and support vector machines	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Biomedical engineering
heal.classification	Ιατρική
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2008009003
heal.identifier.secondary	doi:10.1016/j.compbiomed.2010.12.004
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2011
heal.bibliographicCitation	Ventouras, E., Asvestas, P., karanasiou, I. and Matsopoulos, G. (February 2011). Classification of error-related negativity (ERN) and positivity (PE) potentials using kNN and support vector machines. Computers in Biology and Medicine. 41(2). pp. 98-109. Elsevier Science Ltd. Available from: http://www.sciencedirect.com [Accessed 31/01/2011]	en
heal.abstract	Error processing in subjects performing actions has been associated with the Event-Related Potential (ERP) components called Error-Related Negativity (ERN) and Error Positivity (Pe). In this paper, features based on statistical measures of the sample of averaged ERP recordings are used for classifying correct from incorrect actions. Three feature selection techniques were used and compared. Classification was done by means of a kNN and a Support Vector Machines (SVM) classifier. The use of a leave-one-out approach in the feature selection provided sensitivity and specificity values concurrently higher than or equal to 87.5%, for both classifiers. The classification results were significantly better for the time window that included only the ERN, as compared to time windows including also Pe.	en
heal.publisher	Elsevier Science Ltd	en
heal.journalName	Computers in Biology and Medicine	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true