Tool for MEEG data processing based on Brain Connectivity Variable Resolution Tomographic Analysis (BC-VARETA) Model. See description of BC-VARETA and example in simulations at the link https://github.com/dpazlinares/BC-VARETA.

References:

Paz-Linares, D., Gonzalez-Moreira, E., Martinez-Montes, E. and Valdes-Sosa, P.A., 2018. Note on the Estimation of Embedded Hermitian Gaussian Graphical Models for MEEG Source Activity and Connectivity Analysis in the Frequency Domain. Part I: Single Frequency Component and Subject. arXiv preprint arXiv:1810.01174. https://arxiv.org/abs/1810.01174

Paz-Linares, D., Gonzalez-Moreira, E., Martinez-Montes, E., Valdes-Hernandez, P.A., Bosch-Bayard, J., Bringas-Vega, M.L. and Valdes-Sosa, P.A., 2018. Caulking the Leakage Effect in MEEG Source Connectivity Analysis. arXiv preprint arXiv:1810.00786. https://arxiv.org/abs/1810.00786

CCC-members/BC-VARETA_Toolbox direct sourse:

https://codeload.github.com/CCC-members/BC-VARETA_Toolbox/zip/master

Example of data structure (time series, leadfield, surface, and electrodes) is hosted in Onedrive:

https://lstneuro-my.sharepoint.com/:u:/g/personal/cc-lab_neuroinformatics-collaboratory_org/EQVy7Y3oL9lDqS4_aNwglCsBMngspSuQ6yVudDj1xUOhgA?download=1

Main Function for MEEG real data analysis - Main.m (call this function for run)

Inputs for bash: - configure files: app/processes.json app/properties.json bcv_properties/general_params.json bcv_properties/sensor_params.json bcv_properties/activation_params.json bcv_properties/connectivity_params.json bcv_properties/spectral_params.json

Outputs: - results: subfolder containing the bc-vareta outputs

Complementary Functions - xspectrum: computes the spectra of the simulated scalp activity - bcvareta: executes BC-VARETA method - bcvareta_initial_values: computes 'bcvareta' initialization - screening_ssbl: extracts the posibly active generators as part of 'bcvareta_initial_values', using the Elastic Net Structured Sparse Bayesian Learning - trascendent_term: nonlinear function for regularization parameters estimation within the function 'screening_ssbl'
- screening: applies a smoothing to the outputs of 'screening_ssbl'

Authors:

• Deirel Paz Linares
• Eduardo Gonzalez Moreira
• Ariosky Areces Gonzalez
• Pedro A. Valdes Sosa

Date: September 15, 2018