Estimating Brain Connectivity Using Copula Gaussian Graphical Models

@article{Gao2019EstimatingBC,
  title={Estimating Brain Connectivity Using Copula Gaussian Graphical Models},
  author={Xu Gao and Weining Shen and Chee-Ming Ting and Steven C. Cramer and Ramesh Srinivasan and Hernando C. Ombao},
  journal={2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},
  year={2019},
  pages={108-112}
}
  • Xu Gao, Weining Shen, H. Ombao
  • Published 8 April 2019
  • Computer Science
  • 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)
Electroencephalogram (EEG) has been widely used to study cortical connectivity during acquisition of motor skills. Previous studies using graphical models to estimate sparse brain networks focused on time-domain dependency. This paper introduces graphical models in the spectral domain to characterize dependence in oscillatory activity between EEG channels. We first apply a transformation based on a copula Gaussian graphical model to deal with non-Gaussianity in the data. To obtain a simple and… 

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