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Clustering of Brain Signals

 

  • In this project we develop a method that identifies brain regions that are synchronized during resting state in a sense that these regions share similar oscillations or waveforms. Our proposed method produces clusters of EEGs which serve as a proxy for segmenting the brain cortical surface. 

    We develop a new time series clustering method, which uses the total variation distance as a measure of similarity and the hierarchical merger algorithm as the clustering algorithm. 

  • Researchers: C. Euan  (Lead, CIMAT), H. Ombao (UC Irvine) and J. Ortega (CIMAT)

  • Collaborators: S. Cramer (Neurology, UC Irvine) and D. Moorman (Psychology, UMass).

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