  Electroencephalography signals allow to explore the functional activity of
the brain cortex in a non-invasive way. However, the analysis of these signals
is not straightforward due to the presence of different artifacts and the very
low signal-to-noise ratio. Cross-Frequency Coupling (CFC) methods provide a way
to extract information from EEG, related to the synchronization among frequency
bands. However, CFC methods are usually applied in a local way, computing the
interaction between phase and amplitude at the same electrode. In this work we
show a method to compute PAC features among electrodes to study the functional
connectivity. Moreover, this has been applied jointly with Principal Component
Analysis to explore patterns related to Dyslexia in 7-years-old children. The
developed methodology reveals the temporal evolution of PAC-based connectivity.
Directions of greatest variance computed by PCA are called eigenPACs here,
since they resemble the classical \textit{eigenfaces} representation. The
projection of PAC data onto the eigenPACs provide a set of features that has
demonstrates their discriminative capability, specifically in the Beta-Gamma
bands.
