Understanding the functional organization and execution mechanisms of the brain is one of the key challenges of neuroscience. Functional connectivity emerging from phase synchronization of neural oscillations of different brain regions provides a powerful tool for investigations. While the brain manifests highly dynamic activation patterns, most connectivity work is based on the assumption of signal stationarity. One of the underlying reasons is the problem of obtaining high temporal and spectral resolution at the same time. Dynamic brain connectivity seeks to uncover the dynamism of brain connectivity but the common sliding window methods provide poor temporal resolution, not detailed enough for studying fast cognitive tasks. This paper proposes the use of the Complete Ensemble Empirical Mode Decomposition followed by Hilbert transformation to extract instantaneous frequency and phase information |
