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MEG
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  • Deirel Paz-Linares ,
  • Eduardo Gonzalez-Moreira ,
  • Ariosky Areces Gonzalez ,
  • Ying Wang ,
  • Min Li ,
  • Eduardo Martínez-Montes ,
  • Maria L Bringas-Vega ,
  • Mitchell Valdés-Sosa ,
  • Pedro Valdés-Sosa
Deirel Paz-Linares
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Eduardo Gonzalez-Moreira
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Ariosky Areces Gonzalez
The Clinical Hospital of Chengdu Brain Science Institute

Corresponding Author:[email protected]

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Ying Wang
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Eduardo Martínez-Montes
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Maria L Bringas-Vega
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Mitchell Valdés-Sosa
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Pedro Valdés-Sosa
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Abstract

Identifying the connectivity of functional networks underpinning undirectly observed phenomena for neurosciences or other fields poses a Bayesian inverse-problem. Electromagnetic (EEG or MEG) inverse-solutions unveil the cortical oscillatory networks that strongly correlate to brain function with a spectral transparency that no other in vivo neuroimage may provide. Simulations of such an inverse-problem also reveal distortions of the connectivity determined by most common state-of-the-art solutions. We disclose the origin of distortions and remedy them via a Hidden Gaussian Graphical Spectral (HIGGS) model, the Bayesian formalism for the inverse-problem in identifying such networks. In human EEG alpha rhythm simulations, distortions measured as ROC performance do not surpass the 2% in our HIGGS solution and reach 20% in state-of-the-art approaches. Congruence in macaque simultaneous EEG/ECoG recordings provides experimental confirmation for our solution with 1/3 more congruence than state-of-the-art methods.