Abstract
The human brain, which is one of the most complex organic systems, involves billions of interacting physiological and chemical processes that give rise to experimentally observed neuroelectrical activity, which is called an electroencephalogram (EEG). The presence of non-stationarity and intermittency render standard available methods unsuitable for detecting hidden dynamical patterns in the EEG. In this paper, a method that is suitable for non-stationary signals and preserving the phase characteristics and that combines wavelet and Hilbert transforms was applied to multivariate EEG signals from human subjects at rest as well as in different cognitive states: listening to music, listening to text and performing spatial imagination. It was found that, if suitably rescaled, the gamma band EEG over distributed brain areas while listening to music can be described by a universal and homogeneous scaling, whereas this homogeneity in scale is reduced at resting conditions and also during listening to text and performing spatial imagination. The degree of universality is characterized by a Kullback-Leibler divergence measure. By statistical surrogate analysis, nonlinear phase interaction was found to play an important role in exhibiting universality among multiple cortical regions.