The main software used by the MEG Lab is Brainstorm, a Matlab-based open-source toolbox for MEG/EEG data visualization and analysis.
Network Analysis in Brainstorm
Brainstorm now supports brain connectivity analysis. We have developed functions that partition brain networks using modularity and display the results through the Brainstorm interface. You can download the functions from here.
A brief tutorial: after performing source reconstruction in Brainstorm, load a set of predefined scouts. Drop a source map in the process window and run process->extract scout timeseries (select all and concatenate in one unique matrix). Drop the extracted timeseries to the process window and run process->connectivity->coherence NxN. Results plot in a circular form with colored ROIs indicating cluster membership according to modularity partitioning. A visual summary is available here.
Assessing the Modular Structure of Brain Networks
Compared with the popularity of methods to detect community structure, only a few methods exist to statistically control for spurious modules, relying almost exclusively on resampling techniques. In our recent work, we show that the modularity of random networks follows a transformed version of the Tracy-Widom distribution, providing for the first time a link between module detection and random matrix theory. Open source code implementing the modularity-based partitions and statistical testing is available here. This work supported by the National Science Foundation under grant BCS-1134780.
A novel spatial BSS method tailored for application to the cerebral cortex based on the Second Order Blind Identification (SOBI) method. Software and example data are available here.