  The human brain is a large-scale network which function depends on dynamic
interactions between spatially-distributed regions. In the rapidly-evolving
field of network neuroscience, two yet unresolved challenges are potential
breakthroughs. First, functional brain networks should be estimated from
noninvasive and easy to use neuroimaging techniques. Second, the time/space
resolution of these techniques should be high enough to assess the dynamics of
identified networks. Emerging evidence suggests that Electroencephalography
(EEG) source connectivity method may offer solutions to both issues provided
that scalp EEG signals are appropriately processed. Therefore, the performance
of EEG source connectivity method strongly depends on signal processing (SP)
that involves various methods such as preprocessing techniques, inverse
solutions, statistical couplings between signals and network science. The main
objective of this tutorial-like review is to provide an overview on EEG source
connectivity. We describe the major contributions that the SP community brought
to this research field. We emphasize the methodological issues that need to be
carefully addressed to obtain relevant results and we stress the current
limitations that need further investigation. We also report results obtained in
concrete applications, in both normal and pathological brain states. Future
directions in term of signal processing methods and applications are eventually
provided
