EEGNET: An Open Source Tool for Analyzing and Visualizing M/EEG Connectome
Publication Date
September 17, 2015
Journal
PLOS ONE
Authors
Mahmoud Hassan, Mohamad Shamas, Mohamad Khalil, Wassim El Falou, et al
Volume
10
Issue
9
Pages
e0138297
DOI
https://dx.plos.org/10.1371/journal.pone.0138297
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0138297
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/26379232
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4574940
Europe PMC
http://europepmc.org/abstract/MED/26379232
Web of Science
000361769400081
Scopus
84945928881
Mendeley
http://www.mendeley.com/research/eegnet-open-source-tool-analyzing-visualizing-meeg-connectome
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Mendeley | Further Information

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Scopus | Further Information

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/2269490"], "description"=>"<p>The M/EEG data are imported (256 dense EEG signals is this example). The functional connectivity is then computed directly between the scalp signals. Network measures can be extracted from the adjacency matrix and the scalp network can be visualized in an interactive way. On the other hand, the original M/EEG data can be used to estimate the brain sources by solving the inverse problem. Functional connectivity measures can be applied on the reconstructed sources. Graph measures can also be computed and the correspondent cortex network can be visualized. Node’s size and color can be used to encode any chosen network measures (their strength for instance) as well as the edges (their weight for instance).</p>", "links"=>[], "tags"=>["computation", "source", "graph theory analysis", "novel software package", "EEGNET", "matlab", "Open Source Tool", "tool", "network measures", "brain networks"], "article_id"=>1547514, "categories"=>["Biological Sciences"], "users"=>["Mahmoud Hassan", "Mohamad Shamas", "Mohamad Khalil", "Wassim El Falou", "Fabrice Wendling"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0138297.g001", "stats"=>{"downloads"=>0, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Basic_workflow_of_EEGNET_/1547514", "title"=>"Basic workflow of EEGNET.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-17 03:38:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/2269503"], "description"=>"<p>A. The signals were loaded to EEGNET and the averaged signal over trials was visualized. The connectivity measure was chosen (PLV in this case) and the frequency bands were set to 30–45Hz (Low Gamma band). B. The network obtained at scalp level in the period 120–150ms. C. The network obtained at the same period after source reconstruction using wMNE and connectivity measurement (using PLV). D. The degree value of each nodes (based on Destrieux Atlas) was computed and visualized. E. The network obtained at 190–320ms after source reconstruction using wMNE and connectivity measurement (using PLV) and F. the corresponding degree values. Node’s color and size represent the modularity and the degree respectively. The time periods were chosen based on automatic segmentation of such cognitive task [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138297#pone.0138297.ref047\" target=\"_blank\">47</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138297#pone.0138297.ref074\" target=\"_blank\">74</a>].</p>", "links"=>[], "tags"=>["computation", "source", "graph theory analysis", "novel software package", "EEGNET", "matlab", "Open Source Tool", "tool", "network measures", "brain networks"], "article_id"=>1547522, "categories"=>["Biological Sciences"], "users"=>["Mahmoud Hassan", "Mohamad Shamas", "Mohamad Khalil", "Wassim El Falou", "Fabrice Wendling"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0138297.g006", "stats"=>{"downloads"=>0, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Identification_of_brain_networks_involved_in_picture_naming_task_/1547522", "title"=>"Identification of brain networks involved in picture naming task.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-17 03:38:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/2269497"], "description"=>"<p>A) The different steps performed to obtain a ‘static’ scalp network, B) typical example of the dynamics of scalp networks obtained during a picture naming task (see [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138297#pone.0138297.ref047\" target=\"_blank\">47</a>] for about the data). The node color represents the modules and the size represents the strength values.</p>", "links"=>[], "tags"=>["computation", "source", "graph theory analysis", "novel software package", "EEGNET", "matlab", "Open Source Tool", "tool", "network measures", "brain networks"], "article_id"=>1547516, "categories"=>["Biological Sciences"], "users"=>["Mahmoud Hassan", "Mohamad Shamas", "Mohamad Khalil", "Wassim El Falou", "Fabrice Wendling"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0138297.g002", "stats"=>{"downloads"=>2, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scalp_networks_/1547516", "title"=>"Scalp networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-17 03:38:07"}
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{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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