Detecting EEG outliers for BCI on the Riemannian manifold using spectral clustering
Detecting EEG outliers for BCI on the Riemannian manifold using spectral clustering
复制标题
使用谱聚类检测黎曼流形上 BCI 的 EEG 异常值
DOI：
10.1109/embc44109.2020.9175456
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Lotte Fabien
中科院分区：
文献类型：
--
作者：
Yamamoto Maria Sayu;Sadatnejad Khadijeh;Tanaka Toshihisa;Islam Rabiul;Tanaka Yuichi;Lotte Fabien
脑电离群点的自动检测和去除是设计健壮的脑机接口的关键。本文提出了一种基于样本协方差阵的黎曼流形上的离群点检测方法。现有的离群值检测方法存在错误地将一些样本排除为离群值的风险，即使没有离群值，这是因为检测是基于参考矩阵和阈值的。为了解决这一局限性，我们的方法，黎曼谱聚类(RISC)，通过将SCM聚类成非离群点和离群点，基于提出的相似性度量来检测离群点。这考虑了空间的黎曼几何，放大了非离群点集群内的相似性，并削弱了非离群点和离群点集群之间的相似性，而不是设置阈值。为了评估RISC的性能，我们生成了被不同离群点强度和数字污染的人工脑电数据集。比较了RISC和现有孤立点检测方法的命中率和误检率(HF)，证实了RISC能够更好地检测孤立点(p<；；0.001)。特别是，对于具有最严重离群值污染的数据集，RISC对HF差异的改善最大。
Automatically detecting and removing Electroencephalogram (EEG) outliers is essential to design robust brain-computer interfaces (BCIs). In this paper, we propose a novel outlier detection method that works on the Riemannian manifold of sample covariance matrices (SCMs). Existing outlier detection methods run the risk of erroneously rejecting some samples as outliers, even if there is no outlier, due to the detection being based on a reference matrix and a threshold. To address this limitation, our method, Riemannian Spectral Clustering (RiSC), detects outliers by clustering SCMs into non-outliers and outliers, based on a proposed similarity measure. This considers the Riemannian geometry of the space and magnifies the similarity within the non-outlier cluster and weakens it between non-outlier and outlier clusters, instead of setting a threshold. To assess RiSC performance, we generated artificial EEG datasets contaminated by different outlier strengths and numbers. Comparing Hit-False (HF) difference between RiSC and existing outlier detection methods confirmed that RiSC could detect outliers significantly better (p <; 0.001). In particular, RiSC improved HF difference the most for datasets with the most severe outlier contamination.