Quantifying EEG synchrony using copulas

  title={Quantifying EEG synchrony using copulas},
  author={S. G. Iyengar and J. Dauwels and P. Varshney and A. Cichocki},
  journal={2010 IEEE International Conference on Acoustics, Speech and Signal Processing},
  • S. G. Iyengar, J. Dauwels, +1 author A. Cichocki
  • Published 2010
  • Computer Science
  • 2010 IEEE International Conference on Acoustics, Speech and Signal Processing
  • In this paper, we consider the problem of quantifying synchrony between multiple simultaneously recorded electroencephalographic signals. These signals exhibit nonlinear dependencies and non-Gaussian statistics. A copula based approach is presented to model the joint statistics. We then consider the application of copula derived synchrony measures for early diagnosis of Alzheimer's disease. Results on real data are presented. 

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    Publications referenced by this paper.
    Neural Synchrony in Brain Disorders: Relevance for Cognitive Dysfunctions and Pathophysiology
    • 1,555
    • PDF
    Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field
    • 1,012
    • PDF
    A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG
    • 306
    • PDF
    An Introduction to Copulas
    • 2,905
    EEG dynamics in patients with Alzheimer's disease
    • 892
    • PDF
    Pattern Classification
    • 8,704
    • PDF
    Causal influence: advances in neurosignal analysis.
    • 97
    • PDF
    The t copula and related copulas
    • 776
    • PDF
    Learning in Graphical Models
    • 1,569
    • PDF