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Clustering Brain Signals: a Robust Approach Using Functional Data Ranking

Clustering Brain Signals: a Robust Approach Using Functional Data Ranking 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 measures: distance of the functional medians and the area of the central region. The performance of the proposed algorithms is examined by simulation studies. We show that, when contaminations are present, the proposed methods for clustering spectral densities are more robust than the mean-based methods. The developed methods are applied to two stages of resting state EEG data from a male college student, corresponding to early exploration of functional connectivity in the human brain. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Classification Springer Journals

Clustering Brain Signals: a Robust Approach Using Functional Data Ranking

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References (30)

Publisher
Springer Journals
Copyright
Copyright © The Classification Society 2020
ISSN
0176-4268
eISSN
1432-1343
DOI
10.1007/s00357-020-09382-1
Publisher site
See Article on Publisher Site

Abstract

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 measures: distance of the functional medians and the area of the central region. The performance of the proposed algorithms is examined by simulation studies. We show that, when contaminations are present, the proposed methods for clustering spectral densities are more robust than the mean-based methods. The developed methods are applied to two stages of resting state EEG data from a male college student, corresponding to early exploration of functional connectivity in the human brain.

Journal

Journal of Classification – Springer Journals

Published: Oct 1, 2021

Keywords: Central region; Functional median; Robustness; Spectral analysis; Time series clustering

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