BESA Connectivity

BESA Connectivity 2.0 provides optimized, user-guided workflows for time-frequency and connectivity analysis of EEG/MEG data. Multiple well-established methods are provided. They were optimized for stream-lined performance that yields results for multiple combinations of input data types, time-frequency methods, and connectivity measures.

Particular highlights include:

  • Four time-frequency methods and nine connectivity methods supported, including Imaginary Part of Coherency, Phase Lag Index, Granger Causality, PDC, DTF
  • Batch mode enabling multi-subject analysis of up to ten different conditions
  • Grand Average visualization
  • Direct comparison between subjects, conditions, time-frequency methods, connectivity methods with one or two mouse clicks
  • Support for source montages as well as sensor-level data and polygraphic channels
  • Superior visualization of results in 2D and 3D
  • Several connectivity visualization modes including connectome view, circular graph view, 3D view
  • Highly versatile image and video export of results
  • ASCII data result export and input support for Matlab
  • Full (multi-subject) project exports for direct reading into BESA Statistics
  • Modern 64-bit architecture optimized for multi-core processing
  • Workflow-based user guidance for optimized usability

The list of new features and changes to previous versions of BESA Connectivity 2.0 is provided in the BESA Connectivity 2.0 – Update History.

Please refer to the BESA Image brochure, pp 21 ff for detailed information.
A short guide to the usage of BESA Connectivity is given in the document Steps for using BESA Connectivity.
You can download the complete BESA Connectivity manual here.

BESA Connectivity integrates optimally with data that were analyzed in BESA Research 7.0 or higher, but it can also process data from other software packages as long as they conform to the BESA Connectivity file format.

The program is fully integrated into the BESA Research Complete package, but can also be ordered as a standalone product.

Brain connectivity in source space

Connectivity in sensor space