论文标题
聚类大脑信号：使用功能数据排名的强大方法
Clustering Brain Signals: A Robust Approach Using Functional Data Ranking
论文作者
论文摘要
在本文中，我们分析了脑电图（EEG），这是脑电活动的记录。我们开发了新的聚类方法来识别同步的大脑区域，其中脑电图根据其光谱密度显示相似的振荡或波形。我们将许多时期或试验的估计光谱密度视为功能数据，并根据功能数据排名开发聚类算法。提出的两个聚类算法采用不同的差异度量：功能中值和中央区域面积的距离。通过模拟研究检查了所提出的算法的性能。我们表明，当存在污染时，簇光谱密度的提出的方法比基于均值的方法更健壮。开发的方法应用于男性大学生的两个静止状态脑电图数据的两个阶段，与早期探索人脑功能连通性相对应。
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.
