EEGNET

EEGNET analyzes Magneto/Electroencephalography (M/EEG) data to compute functional connectivity and graph-theoretical measures of cortical and scalp-level brain networks.

Key Features:

• Preprocessing: Performs essential preprocessing of M/EEG signals to prepare data for subsequent analysis.
• Inverse Problem Solution: Solves the EEG/MEG inverse problem to localize and reconstruct cortical sources from scalp recordings.
• Functional Connectivity Computation: Computes functional connectivity among signals recorded at surface electrodes or among reconstructed cortical sources.
• Graph Theory Analysis: Computes graph-theoretical network measures to quantify network properties.
• Network Visualization: Produces visualizations of functional brain networks and associated graph metrics.

Scientific Applications:

• Connectome Dynamics: Investigating large-scale connectome dynamics and the spatial and temporal organization of brain networks using M/EEG.
• Source-Level Network Analysis: Analyzing functional connectivity and network topology at reconstructed cortical source level for localization studies.
• Scalp-Level Network Analysis: Studying functional interactions and network measures directly at scalp electrode locations in sensor-space analyses.
• Graph-Theoretical Characterization: Characterizing network topology and deriving network metrics for neuroscience research.

Methodology:

Implemented in MATLAB; performs signal preprocessing, solves the inverse problem to reconstruct cortical sources, computes functional connectivity among electrodes or reconstructed sources, applies graph-theoretical measures, and generates network visualizations.