A novel methodology to quantify dense EEG in cognitive tasks
Cognition emerges from complex interaction amongst widespread brain areas. In this paper, we use a novel methodology for temporal networks quantification for EEG. We model the spatiotemporal structure of dependencies across different electrodes with respect to a single electrode as a local probability density function. This enables immediately the use of information theoretic quantities (information divergences) to quantify brain connectivity in simple two-dimensional graphs. We show that for a visual-motor-driven task, we are able to cluster subjects that performed the task with higher attention-coefficient, in an unsupervised-fashion. We test this methodology with two measures of functional connectivity: correlation coefficient and a measure of association.