A novel single-trial analysis scheme for characterizing the presaccadic brain activity based on a SON representation

Bozas, K; Dimitriadis, SI; Laskaris, NA; Tzelepi, A

dc.contributor.author	Bozas, K	en
dc.contributor.author	Dimitriadis, SI	en
dc.contributor.author	Laskaris, NA	en
dc.contributor.author	Tzelepi, A	en
dc.date.accessioned	2014-03-01T02:46:39Z
dc.date.available	2014-03-01T02:46:39Z
dc.date.issued	2010	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32757
dc.subject	Brain Activation	en
dc.subject	Brain Imaging	en
dc.subject	Complex Network	en
dc.subject	Functional Connectivity	en
dc.subject	Self Organization	en
dc.subject	Neural Network	en
dc.subject.other	Brain activation	en
dc.subject.other	Brain activity	en
dc.subject.other	Brain imaging	en
dc.subject.other	Brain regions	en
dc.subject.other	Complex network analysis	en
dc.subject.other	EOG signal	en
dc.subject.other	Experimental control	en
dc.subject.other	Functional connectivity	en
dc.subject.other	Go/no-go	en
dc.subject.other	Ocular movements	en
dc.subject.other	Self-organizing neural network	en
dc.subject.other	Single-trial analysis	en
dc.subject.other	Brain	en
dc.subject.other	Electric network analysis	en
dc.subject.other	Eye movements	en
dc.subject.other	Linear transformations	en
dc.subject.other	Neural networks	en
dc.title	A novel single-trial analysis scheme for characterizing the presaccadic brain activity based on a SON representation	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-642-15822-3_44	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-15822-3_44	en
heal.publicationDate	2010	en
heal.abstract	We introduce a tactic for single-trial (ST) analysis that incorporates, in the study of saccades, the experimental control of a behavioural variable within the standard paradigm of a repeated execution of a single task. The ubiquitous ST-variability in brain imaging recordings is turned, here, to an additional informative dimension that can be exploited to gain further understanding of brain's function mechanisms. Our approach builds over a self-organizing neural network (SON) that can efficiently learn and parameterise the variability in the patterning of electro-oculographic (EOG) signals. In a second stage, the STs of encephalographic activity are organized accordingly and the observed variations in the EOG signals are associated with specific brain activations. Finally, complex network analysis is employed as a means to characterize the ST-variability based on modes of functional connectivity. Using EEG data from a Go/No-Go paradigm, we demonstrate that the spontaneous variations in the execution of a saccade can open a window on the role of different brain regions for ocular movements. © 2010 Springer-Verlag Berlin Heidelberg.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-642-15822-3_44	en
dc.identifier.volume	6353 LNCS	en
dc.identifier.issue	PART 2	en
dc.identifier.spage	362	en
dc.identifier.epage	371	en