MULTI-FREQUENCY MULTI-LAYER BRAIN NETWORK TOPOLOGY STUDY: DIFFERENTIATE PRE-MUSIC AND POST MUSIC RESTING CONDITION.

We analyzed dynamic functional brain connectivity by adopting the sliding window approach using imaginary part of
phase locking value (iPLV) for EEG. Both intra and cross-frequency couplings (CFC) namely phase-to-amplitude were
estimated using iPLV/WC at every snapshot of the dynamic functional brain connectivity (DFBC). The analysis was
done on the Pre and Post brain resting state upon exposure to a pleasant music. Construction of a single integrated dynamic functional connectivity graph (IDFCG) that preserves both the strength of the connections between every
pair of sensors but also the type of dominant intrinsic coupling modes (DICM) was done.Using proper surrogate analysis, we defined the dominant intrinsic coupling mode (DICM) per pair of regions-of-interest (ROI). Neural-gas algorithm will be used for encoding brain activity spatio-temporal dynamics in the form of a symbolic time series. The symbolic time series derived in this way is mapped to a network, the topology of which encapsulates the most
important phase transitions of the underlying dynamical system