The IAS-MEEG Package: A Flexible Inverse Source Reconstruction Platform for Reconstruction and Visualization of Brain...

Area of Science:

• Neuroscience
• Computational Neuroscience
• Biomedical Engineering

Background:

• Reconstructing neural activity from electroencephalography (EEG) and magnetoencephalography (MEG) data is crucial for understanding brain function.
• Existing methods often require complex parameter tuning or lack integrated visualization capabilities.
• The need for computationally efficient and user-friendly tools for neural activity reconstruction persists.

Purpose of the Study:

• To present a standalone Matlab software platform for reconstructing neural activity from MEG and EEG data.
• To provide integrated visualization tools for the reconstructed neural activity.
• To offer a flexible and computationally efficient solution suitable for lengthy time series analysis.

Main Methods:

• Developed a software platform in Matlab incorporating hierarchical Bayesian models and Krylov subspace iterative least squares solvers for neural activity inversion.
• Integrated anatomical information and prior beliefs about focality into the Bayesian framework.
• Designed the software for computational efficiency and automatic parallelization using Matlab's capabilities.

Main Results:

• Demonstrated the platform's flexibility in reconstructing neural activity patterns of varying sizes from both MEG and EEG data.
• Successfully reconstructed activity in both cortical and subcortical brain structures.
• The software requires minimal user input while allowing control over solution focality and accuracy.

Conclusions:

• The developed Matlab platform provides an efficient and flexible tool for neural activity reconstruction from MEG/EEG data.
• The integrated inverse solver and visualization modules offer a comprehensive solution for neuroscience research.
• The software, including a Brainstorm plugin, is publicly available on Github with documentation and test data.