<p>Mean values with bars corresponding to the standard errors of the efficiency-cost curves are plotted for different frequency bands including theta (3–7 Hz), alpha (7–13 Hz), beta (13–30 Hz) and gamma (30–50 Hz).The graphs show the metrics for the functional networks obtained from EEG time series of 30 healthy subjects (black lines) and the randomized networks (cyan lines). The randomized networks have the same degree sequence as the original networks. The red asterisk above the plots represent the density value for which the two populations have significantly different median (Wilcoxon’s ranksum test; P < 0.05).</p
<p>Bars show the differences in the areas under curves (AUC) of (A) small-world parameters (Cp, Lp, ...
<p>Strength (<i>S</i>) Clustering (<i>C</i>) and global efficiency (<i>E</i>) for the PLI index for ...
Objective: Using EEG to characterise functional brain networks through graph theory has gained signi...
<p>(<i>A</i>) Cost-efficiency in the -, -, -, -, and cross-frequency band networks for healthy contr...
<p>The functional brain networks showed higher local efficiency than that of the matched random netw...
<p>Global and local efficiency (<i>y</i>-axis) as a function of cost (<i>x</i>-axis) for a random gr...
Functional connectivity in human brain can be represented as a network using electroencephalography ...
<p>Global (E<sub>g</sub>) and local (E<sub>l</sub>) efficiencies are depicted as a function of wired...
<p>(A) EEG time-series from the 26 scalp electrodes were separately filtered for the delta, theta, a...
<p>A: Various network measures were determined for networks in different frequency ranges as a funct...
In the first study, functional brain networks are derived from magnetoencephalography (MEG) data fro...
<p>Panels plot group-wise graph-theoretic metrics averaged over all connection densities considered....
The extraction of the salient characteristics from brain connectivity patterns is an open challengin...
Graph theory provides many metrics of complex network organization that can be applied to analysis o...
<p>The brain networks under each condition showed higher local efficiency than the matched random ne...
<p>Bars show the differences in the areas under curves (AUC) of (A) small-world parameters (Cp, Lp, ...
<p>Strength (<i>S</i>) Clustering (<i>C</i>) and global efficiency (<i>E</i>) for the PLI index for ...
Objective: Using EEG to characterise functional brain networks through graph theory has gained signi...
<p>(<i>A</i>) Cost-efficiency in the -, -, -, -, and cross-frequency band networks for healthy contr...
<p>The functional brain networks showed higher local efficiency than that of the matched random netw...
<p>Global and local efficiency (<i>y</i>-axis) as a function of cost (<i>x</i>-axis) for a random gr...
Functional connectivity in human brain can be represented as a network using electroencephalography ...
<p>Global (E<sub>g</sub>) and local (E<sub>l</sub>) efficiencies are depicted as a function of wired...
<p>(A) EEG time-series from the 26 scalp electrodes were separately filtered for the delta, theta, a...
<p>A: Various network measures were determined for networks in different frequency ranges as a funct...
In the first study, functional brain networks are derived from magnetoencephalography (MEG) data fro...
<p>Panels plot group-wise graph-theoretic metrics averaged over all connection densities considered....
The extraction of the salient characteristics from brain connectivity patterns is an open challengin...
Graph theory provides many metrics of complex network organization that can be applied to analysis o...
<p>The brain networks under each condition showed higher local efficiency than the matched random ne...
<p>Bars show the differences in the areas under curves (AUC) of (A) small-world parameters (Cp, Lp, ...
<p>Strength (<i>S</i>) Clustering (<i>C</i>) and global efficiency (<i>E</i>) for the PLI index for ...
Objective: Using EEG to characterise functional brain networks through graph theory has gained signi...