In dynamical systems, the information flows converge or diverges in state space and is integrated or communicated between different cells assemblies termed as CFC. This process allows different oscillatory systems to communicate in accurate time, control and distribute the information flows in cell assemblies. The CF interactions allow the oscillatory rhythms to communicate in accurate time, and reintegrate the separated information. The intrinsic brain dynamics in Electroencephalography (EEG) with eye - closed (EC) and eye open (EO) during resting states have been investigated to see the changes in brain complexity i.e. simple visual processing which are associated with increase in global dimension complexity. In order to study these changes in EEG, we have computed the coupling to see the inhibitory interneurons response and inter-regions functional connectivity differences between the eye conditions. We have investigated the fluctuations in EEG activities in low (delta, theta) and high (alpha) frequency brain oscillations. Coupling strength was estimated using Dynamic Bayesian inference approach which can effectively detect the phase connectivity subject to the noise within a network of time varying coupled phase oscillators. Using this approach, we have seen that delta-alpha and theta-alpha CFC are more dominant in resting state EEG and applicable to multivariate network oscillator. It shows that alpha phase was dominated by low frequency oscillations i.e. delta and theta. These different CFC help us to investigate complex neuronal brain dynamics at large scale networks. We observed the local interactions at high frequencies and global interactions at low frequencies. The alpha oscillations are generated from both posterior and anterior origins whereas the delta oscillations found at posterior regions.