In this paper, we analyze electroencephalograms (EEG) 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.

中文翻译：

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聚类大脑信号：使用功能数据排名的稳健方法

在本文中，我们分析了脑电活动记录的脑电图 (EEG)。我们开发了新的聚类方法来识别同步的大脑区域，其中 EEG 根据其频谱密度显示出类似的振荡或波形。我们将来自许多时期或试验的估计光谱密度视为功能数据，并开发基于功能数据排序的聚类算法。两种提议的聚类算法使用不同的差异度量：功能中位数的距离和中心区域的面积。所提出的算法的性能通过模拟研究来检验。我们表明，当存在污染时，所提出的光谱密度聚类方法比基于均值的方法更稳健。