Spectral Synchronicity in Brain Signals

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  • Author: Carolina Euán, Hernando Ombao and Joaquín Ortega
  • Date: 10 April 2019
  • Copyright: Image copyright of Patrick Rhodes

A paper published in Statistics in Medicine addresses the problem of identifying brain regions with similar oscillatory patterns detected from electroencephalograms. The authors introduce the hierarchical spectral merger (HSM) clustering method where the feature of interest is the spectral curve and the similarity metric used is the total variance distance.

The paper is available via the link below and the authors explain their findings in further detail below:

Spectral synchronicity in brain signals

Carolina Euán, Hernando Ombao and Joaquín Ortega

Volume 37, Issue 19, 30 August 2018, pages 2855-2873

thumbnail image: Spectral Synchronicity in Brain Signals

The goal of this paper is to develop a clustering method that identifies cortical brain networks that are active under various cognitive experimental conditions. The Hierarchical Spectral Merger (HSM) method, proposed in this paper, groups together those brain signals from different channels that display similar oscillatory patterns.

The main interest is to identify those signals that share similar oscillatory patterns. This task is challenging due to the high temporal resolution of the data and the multiple replicates of the process, which results in a large amount of data. The HSM clustering method takes advantage of the high temporal resolution of the data by transforming it into the frequency space to perform the clustering task. The applicability of the HSM methodology is shown with the analysis of two different electroencephalograms (EEG) data sets.

Together with the clustering tool developed, a methodology based on affinity matrices is proposed to summary the clustering information when several replicates are available. HSM is a general clustering method for stationary time series that may be of interest in other problems. Implementation in R is freely available at https://es.kaust.edu.sa/Pages/CarolinaEuan.aspx

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