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

摘要

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 encephalography 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.