HERMES: towards an integrated toolbox to characterize functional and effective brain connectivity

Area of Science:

• Neuroscience
• Computational Biology
• Signal Processing

Background:

• Time series analysis is crucial for understanding dynamical systems and neural network interactions.
• Traditional methods like cross-correlation and Granger Causality have limitations in capturing complex brain connectivity.
• Advances in synchronization concepts and information theory necessitate new analytical tools.

Purpose of the Study:

• To develop a unified, user-friendly software package for analyzing functional (FC) and effective connectivity (EC).
• To provide neuroscientists and researchers with easy access to advanced time series analysis methods.
• To facilitate the study of brain connectivity from multivariate neurophysiological data.

Main Methods:

• Development of the HERMES toolbox for MATLAB.
• Integration of diverse analytical tools for time series analysis.
• Inclusion of visualization and statistical methods for multiple comparisons.

Main Results:

• The HERMES toolbox offers a comprehensive suite of methods for brain connectivity analysis.
• It supports the study of functional and effective connectivity using EEG and MEG data.
• Includes visualization and statistical tools for robust analysis.

Conclusions:

• HERMES provides an integrated platform for advanced brain connectivity research.
• The toolbox simplifies the application of complex time series analysis methods for neuroscientists.
• It is expected to significantly aid researchers in the emerging field of brain connectivity analysis.