Title: Harnessing functional segregation across brain rhythms as a means to detect EEG oscillatory multiplexing during music listening,
Abstract: Music, being a multifaceted stimulus evolving at multiple timescales,
modulates brain function in a manifold way that encompasses not only the
distinct stages of auditory perception but also higher cognitive processes like
memory and appraisal. Network theory is apparently a promising approach to
describe the functional reorganization of brain oscillatory dynamics during
music listening. However, the music induced changes have so far been examined
within the functional boundaries of isolated brain rhythms. Using naturalistic
music, we detected the functional segregation patterns associated with
different cortical rhythms, as these were reflected in the surface EEG
measurements. The emerged structure was compared across frequency bands to
quantify the interplay among rhythms. It was also contrasted against the
structure from the rest and noise listening conditions to reveal the specific
components stemming from music listening. Our methodology includes an efficient
graph-partitioning algorithm, which is further utilized for mining prototypical
modular patterns, and a novel algorithmic procedure for identifying switching
nodes that consistently change module during music listening. Our results
suggest the multiplex character of the music-induced functional reorganization
and particularly indicate the dependence between the networks reconstructed
from the {\delta} and {\beta}H rhythms. This dependence is further justified
within the framework of nested neural oscillations and fits perfectly within
the context of recently introduced cortical entrainment to music. Considering
its computational efficiency, and in conjunction with the flexibility of in
situ electroencephalography, it may lead to novel assistive tools for real-life
applications.