  Multichannel electroencephalograms (EEGs) have been widely used to study
cortical connectivity during acquisition of motor skills. In this paper, we
introduce copula Gaussian graphical models on spectral domain to characterize
dependence in oscillatory activity between channels. To obtain a simple and
robust representation of brain connectivity that can explain the most variation
in the observed signals, we propose a framework based on maximizing penalized
likelihood with Lasso regularization to search for the sparse precision matrix.
To address the optimization problem, graphical Lasso, Ledoit-Wolf and sparse
estimation of a covariance matrix (SPCOV) algorithms were modified and
implemented. Simulations show the benefit of using the proposed algorithms in
terms of robustness and small estimation errors. Furthermore, analysis of the
EEG data in a motor skill task conducted using algorithms of modified graphical
LASSO and Ledoit-Wolf, reveal a sparse pattern of brain connectivity among
cortices which is consistent with the results from other work in the
literature.
