Topological Data Analysis for Scalp EEG Signal Processing
Topological Data Analysis for Scalp EEG Signal Processing
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DOI：
10.1109/icsip57908.2023.10270899
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Jingyi Zheng;Ziqin Feng;Yuexin Li;Fan Liang;Xuan Cao;Linqiang Ge
中科院分区：
文献类型：
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作者：
Jingyi Zheng;Ziqin Feng;Yuexin Li;Fan Liang;Xuan Cao;Linqiang Ge
拓扑数据分析是一种快速发展和有前途的方法，最近在数据科学领域越来越受欢迎。它利用拓扑和几何度量来描述复杂数据的结构，例如形状，这是数据建模的基础和重要的。头皮脑电图（EEG）被广泛应用于临床试验和科学研究中，以测量大脑活动。然而，头皮脑电信号的分析和建模仍然是一个开放的领域，由于复杂和非平稳的性质，脑电信号本身以及转换后的信号。因此，在本文中，我们提出了一个基于拓扑的处理管道，利用持久的同源性来捕获转换后的EEG信号的底层系统动态，并进一步构建机器学习分类器。利用公开的头皮EEG数据对算法进行了验证，结果表明，拓扑特征能够有效地捕捉到Hilbert-Huang变换所揭示的时频特征的细微变化，ROC曲线下面积达到0.96。
Topological Data Analysis is a fast-growing and promising approach that recently gains popularity in the data science field. It utilizes topological and geometric measurements to describe the structure, for example the shape, of complex data, which is fundamental and important for modeling the data. Scalp Electroencephalography (EEG) is widely used in clinical trials and scientific research to measure the brain activities. However, analyzing and modeling scalp EEG signals is still an open field due to the complex and non-stationary nature of the EEG signal itself as well as the transformed signals. Therefore, in this paper, we propose a topological-based processing pipeline that utilizes persistent homology to capture the underlying system dynamic of the transformed EEG signals and further construct machine learning classifiers. A public available scalp EEG data is used to validate our algorithms, and the results show that the topological features successfully capture the subtle changes in the time-frequency representations revealed by Hilbert-Huang Transformation, with area under ROC curve reaching 0.96.