Clustering Brain Signals: a Robust Approach Using Functional Data Ranking

@article{Chen2020ClusteringBS,
  title={Clustering Brain Signals: a Robust Approach Using Functional Data Ranking},
  author={Tianbo Chen and Ying Sun and Carolina Eu{\'a}n and Hernando C. Ombao},
  journal={Journal of Classification},
  year={2020},
  volume={38},
  pages={425 - 442}
}
In this paper, we analyze electroencephalograms (EEGs) which are recordings of brain electrical activity. We develop new clustering methods for identifying synchronized brain regions, where the EEGs show similar oscillations or waveforms according to their spectral densities. We treat the estimated spectral densities from many epochs or trials as functional data and develop clustering algorithms based on functional data ranking. The two proposed clustering algorithms use different dissimilarity… 

Review of Clustering Methods for Functional Data

A systematic taxonomy is proposed that explores the connections and differences among the existing functional data clustering methods and relates them to the conventional multivariate clustering Methods and is more reliable than existing categorizations.

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