Complex network analysis has an increasing relevance in the study of neurological disorders,
enhancing the knowledge of brain’s structural and functional organization. Network structure
and efficiency reveal different brain states along with different ways of processing the informa-
tion. This work is structured around the exploratory analysis of the brain processes involved
in low-level auditory processing. A complex network analysis was performed on the basis of
brain coupling obtained from electroencephalography (EEG) data, while different auditory stim-
uli were presented to the subjects. This coupling is inferred from the Phase-Amplitude coupling
(PAC) from different EEG electrodes to explore differences between control and dyslexic sub-
jects. Coupling data allows the construction of a graph, and then, graph theory is used to study
the characteristics of the complex networks throughout time for control and dyslexic subjects.
This results in a set of metrics including clustering coefficient, path length and small-worldness.
From this, different characteristics linked to the temporal evolution of networks and coupling are
pointed out for dyslexics. Our study revealed patterns related to Dyslexia as losing the small-
world topology. Finally, these graph-based features are used to classify between control and
dyslexic subjects by means of a Support Vector Machine (SVM).