IEEE Signal Processing - May 2018 - 81
Mahmoud Hassan and Fabrice Wendling
© iS
tock
p
hoto.com/wildpixel
Aiming for high resolution
of brain networks in time
and space
Electroencephalography
Source Connectivity
T
he human brain is a large-scale network the function of
which depends on dynamic interactions between spatially
distributed regions. In the rapidly evolving field of network neuroscience, two unresolved challenges hold the
promise of potential breakthroughs. First, functional brain networks should be identified using noninvasive and easy-to-use
neuroimaging techniques. Second, the time-space resolution of
these techniques should be good enough to assess the dynamics
of the identified networks. Emerging evidence suggests that the
Digital Object Identifier 10.1109/MSP.2017.2777518
Date of publication: 26 April 2018
1053-5888/18©2018IEEE
electroencephalography (EEG) source-connectivity method
may offer solutions to both issues, provided that scalp EEG signals are appropriately processed. Therefore, this technique's
performance strongly depends on signal processing that
involves various methods, such as preprocessing approaches,
inverse solutions, statistical couplings between signals, and network science.
The main objective of this tutorial is to provide an overview
of EEG source connectivity. We describe the major contributions that the signal processing community has brought to this
research field. We emphasize the methodological issues that
need to be carefully addressed to obtain relevant results, and
IEEE Signal Processing Magazine
|
May 2018
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81
Table of Contents for the Digital Edition of IEEE Signal Processing - May 2018
Contents
IEEE Signal Processing - May 2018 - Cover1
IEEE Signal Processing - May 2018 - Cover2
IEEE Signal Processing - May 2018 - Contents
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