Robustness of an event-related potentials classification systen based on the statistical parameters of morphological features

dc.contributor.author	Καλατζής, Ιωάννης	el
dc.contributor.author	Πήλιουρας, Νικόλαος	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Κανδαράκης, Ιωάννης Σ.	el
dc.contributor.author	Βεντούρας, Ερρίκος Μ.	el
dc.date.accessioned	2015-01-29T12:35:47Z
dc.date.issued	2015-01-29
dc.identifier.uri	http://hdl.handle.net/11400/5059
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	Memory mechanisms
dc.subject	Scalp measurements
dc.subject	Μηχανισμοί μνήμης
dc.subject	Μετρήσεις του τριχωτού της κεφαλής
dc.title	Robustness of an event-related potentials classification systen based on the statistical parameters of morphological features	en
heal.type	conferenceItem
heal.generalDescription	Proceedings of the 5th European Symposium on Biomedical Engineering (CD-ROM)	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.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2006
heal.bibliographicCitation	Kalatzis, I., Piliouras, N., Cavouras, D., Kandarakis, I. and Ventouras, E. (2006). Robustness of an event-related potentials classification systen based on the statistical parameters of morphological features. In the 5th European Symposium on Biomedical Engineering. University of Patras: Patras, 2006.	en
heal.abstract	The P600 component is part of the late components of Event-related Potentials (ERPs), which have been related to working memory (WM) mechanisms. The relation of psychiatric illnesses to deficits in WM may manifest itself as a differentiation at the level of the ERP scalp measurements. In the present work, in order to test the robustness of a classification system under various levels of Gaussian noise, ERP activity at 15 leads was simulated creating two sets of templates representing the normal control and patient classes. From these templates a number of representatives of the two classes were produced. Independent Component Analysis (ICA) was applied as a pre-processing step. From the ICA-reconstructed ERPs, in the time window corresponding to the P600 component, five morphological features were extracted and they were used as input features to a Probabilistic Neural Network (PNN) classifier. Results indicate acceptable tolerance of noise, corresponding to overall classification performance levels higher than 80%, up to levels of 20% noise. In most of the best feature combinations and noise level tested, the standard deviation of the amplitude of the P600 component was present, indicating the possible significance of this feature for discrimination in the case of noise-corrupted data.	en
heal.publisher	[χ.ό.]	el
heal.fullTextAvailability	true
heal.conferenceName	European Symposium on Biomedical Engineering	en
heal.conferenceItemType	full paper