EEGNET statistics

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EEGNET specifications


Unique identifier OMICS_10618
Software type Package/Module
Interface Command line interface, Graphical user interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages MATLAB
Computer skills Advanced
Stability Stable
Maintained Yes


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  • person_outline Mahmoud Hassan <>

Publication for EEGNET

EEGNET in publications

PMCID: 5711919
PMID: 29196672
DOI: 10.1038/s41598-017-15082-5

[…] strength were set to zero., based on weighted adjacency matrix, the functional network can be measured by various properties. these properties were computed using the brain connectivity toolbox and eegnet. we calculated the global efficiency, local efficiency, and small-worldness. these indices are crucial for understanding the information sharing, integration, and segregation among the brain […]

PMCID: 5361870
PMID: 28367403
DOI: 10.1016/j.nicl.2017.03.002

[…] combination. this was the case for the wmne/plv.fig. 1fig. 1, the inverse solutions were computed using brainstorm (). the network measures and network visualization were performed using bct () and eegnet () respectively. see fig. s1 in the supplementary materials for more details about the dense-eeg source connectivity method., networks can be illustrated by graphs, which are sets of nodes […]

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EEGNET institution(s)
INSERM, U1099, Rennes, France; Université de Rennes 1, LTSI, France; Lebanese University, AZM Center for Biotechnology Research and Its Applications, Tripoli, Lebanon
EEGNET funding source(s)
This work was supported by AZM and SAADE Association (Tripoli, Lebanon) and the Rennes University Hospital (COREC Project named conneXion, 2012–14). The work has also received a French government support granted to the CominLabs excellence laboratory and managed by the National Research Agency in the “Investing for the Future” program under reference ANR-10-LABX-07-01.

EEGNET review

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Hu Yuxia

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EEGNET is an open source tool to analyze brain networks based on EEG. Includes the following features: i) Basic steps in preprocessing M/EEG signals, ii) the solution of the inverse problem to localize / reconstruct the cortical sources, iii) the computation of functional connectivity among signals collected at surface electrodes or/and time courses of reconstructed sources and iv) the computation of the network measures based on graph theory analysis. EEGNET is easy to use, flexible and user friendly. So I think it is very useful to my study.