Universality in the brain while listening to music

Area of Science:

• Neuroscience
• Complex Systems Analysis
• Signal Processing

Background:

• Human brain activity, measured via electroencephalogram (EEG), is highly complex and non-stationary.
• Standard analysis methods struggle with EEG's non-stationarity and intermittency, hindering pattern detection.
• Understanding brain dynamics requires advanced methods capable of analyzing complex, non-stationary signals.

Purpose of the Study:

• To develop and apply a novel method for analyzing multivariate EEG signals, particularly for non-stationary data.
• To investigate hidden dynamical patterns and scaling properties in EEG across different cognitive states.
• To characterize the universality of brain activity scaling and identify the role of nonlinear phase interactions.

Main Methods:

• A combined wavelet and Hilbert transform method was employed to analyze multivariate EEG signals.
• The method was applied to EEG data from human subjects in resting, music listening, text listening, and spatial imagination states.
• Kullback-Leibler divergence and statistical surrogate analysis were used to quantify universality and phase interactions.

Main Results:

• Gamma band EEG exhibited universal and homogeneous scaling across distributed brain areas during music listening.
• This homogeneity was reduced during resting conditions, text listening, and spatial imagination.
• Nonlinear phase interaction was identified as crucial for exhibiting universality across cortical regions.

Conclusions:

• The developed method effectively analyzes non-stationary EEG signals, revealing distinct scaling properties in different cognitive states.
• Music listening is associated with a unique, universal scaling pattern in gamma band EEG.
• Nonlinear phase interactions are fundamental to the synchronized, universal brain activity observed across cortical regions.