A Novel Single-Trial Analysis Scheme for Characterizing the Presaccadic Brain Activity Based on a SON Representation

  • Konstantinos Bozas
  • Stavros I. Dimitriadis
  • Nikolaos A. Laskaris
  • Areti Tzelepi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6353)


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.


Posterior Parietal Cortex Onset Detection Local Efficiency Saccade Velocity IEEE Signal Processing Magazine 
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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Konstantinos Bozas
    • 1
  • Stavros I. Dimitriadis
    • 1
  • Nikolaos A. Laskaris
    • 1
  • Areti Tzelepi
    • 2
  1. 1.Laboratory of Artificial Intelligence & Information Analysis, Department of InformaticsAristotle University of ThessalonikiGreece
  2. 2.ICCSNational Technical University of AthensGreece

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