Abstract
Biological signals represent patterns of change in the rhythms of biological systems providing a complementary way to study the dynamics of these systems. The research reported in this paper is based on methodological approaches used to quantify the dynamical fluctuations of neurophysiological systems. We use time-frequency representation techniques including spatial coupling and spatial wavelet phase coherence to analyze the dynamics of electroencephalographic (EEG) signals in different frequency bands in the form of topographic maps. The distribution of power in different frequency bands is computed. The findings indicated that the power in delta frequency band is mainly distributed in prefrontal and occipital regions, whereas, power in theta band is distributed in fronto-occipital regions. The power in alpha band is distributed in posterior and extended to anterior, power in beta band in posterior and prefrontal regions and gamma band power is dominant in prefrontal and occipital regions. All the frequency bands are involved in different activities in one way or other, however alpha band power revealed dominant anterior-posterior activity where the eye-closed (EC) coherence is higher than eye open (EO) coherence. The phase-phase CFC on multichannel EEG signals with EC and EO during resting state is also computed to investigate which brain regions are affected by phase modulation of low frequency bands. Coupling in different frequency bands is estimated using dynamic Bayesian inference approach. This approach can detect the phase connectivity within a network of time varying coupled oscillator subject to the noise. The self, direct, and common coupling scheme showed strong coupling in delta-alpha to alpha ( \delta,\alpha \to \alpha ) and theta-alpha to alpha ( \theta,\alpha \to \alpha ) bands. However, moderate couplings are found in theta-gamma to gamma ( \theta,\gamma \to \gamma ), alpha-gamma to gamma ( \alpha,\gamma \to \gamma ), and delta-gamma to gamma \delta,\gamma \to \gamma ) bands.