A novel methodology to quantify dense EEG in cognitive tasks
Catia S. Silva, José C. Príncipe, Andreas Keil
Abstract
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.
BibTeX
@inproceedings{icassp2017_anovelmethodolog,
title = {A novel methodology to quantify dense EEG in cognitive tasks},
author = {Catia S. Silva and José C. Príncipe and Andreas Keil},
booktitle = {ICASSP 2017},
year = {2017}
}