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
Example of data structure (time series, leadfield, surface, and electrodes) is hosted in Onedrive:
Main Function for MEEG real data analysis
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Main (call this function).
Inputs:
- data: subfolder containing the EEG data, leadfield, sufraces.
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 % - Pedro A. Valdes Sosa
% Date: September 15, 2018