Abstract
We review the theory and algorithms of electrophysiological brain connectivity analysis. This tutorial is aimed at providing an introduction to brain functional connectivity from electrophysiological signals, including electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), stereoelectroencephalography (SEEG). Various connectivity estimators are discussed, and algorithms introduced. Important issues for estimating and mapping brain functional connectivity with electrophysiology are discussed.
Highlights
B RAIN function and dysfunction are encoded in networks within the brain that are distributed over 3-dimensional space and evolves in time
We have described various methods for estimating brain functional connectivity from electrophysiological signals and discussed relevant literature
It is generally accepted that functional connectivity analysis should be performed on the source space within the brain, instead of over the scalp, with a sufficient number of sensors
Summary
B RAIN function and dysfunction are encoded in networks within the brain that are distributed over 3-dimensional space and evolves in time. It is of great importance to image brain activation and functional connectivity which are the building blocks of neural information processing Such knowledge plays an important role for neuroscience research and clinical applications of managing various brain diseases. Various neuroimaging modalities have been pursued to achieve the aforementioned goal, including functional magnetic resonance imaging (fMRI), electrophysiological neuroimaging such as electroencephalography (EEG), magnetoencephalography (MEG), and electrocorticography (ECoG), as well as functional near-infrared spectroscopy (fNIRS) and positron emission tomography (PET). Intracranial EEG (iEEG), EEG/MEG, and the source signals reconstructed by EEG/MEG source imaging techniques have been proven efficient for measuring brain functional connectivity between various regions Functional connectivity measures, such as coherence or causal directions, have been used to study brain networks associated with cognitive functions, spontaneous activities and neurological disorders. The merits, limitations, and needs for future development are discussed
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