Identification method of independent components related to artifacts in electroencephalograms
Identification method of independent components related to artifacts in electroencephalograms
复制标题
脑电图中伪影相关独立分量的识别方法
DOI：
10.1002/tee.23010
复制
发表时间：
2019
影响因子：
1
通讯作者：
H. Kadokura
中科院分区：
文献类型：
--
作者：
Shun Hiratsuka;Daisuke Hayasaka;Kazuo Kato;H. Kadokura
在测量脑电图（EEG）时，由于眨眼和眼球运动引起的伪影会污染脑电图信号。独立分量分析（ICA）是解决这一问题的一种方法，它可以从EEG数据中分离出伪成分。然而，与伪影相关的独立成分（ic）的识别，特别是在眨眼和运动产生多重噪声信号的情况下，尚未得到充分的研究。这项工作的目的是识别与EEG数据中眨眼和眼动伪影相关的ic。该方法首先通过叠加眨眼和眼动模板，生成具有28个通道的人工脑电信号，然后将ICA应用于人工脑电信号中，得到集成电路。然后，提取每个IC的峰度特征量以及每个IC与眨眼模板和眼动模板之间的交叉相关系数，并通过K均值聚类进行聚类。因此，与循环ic的结果相比，确定了与多个工件相关的最合理的ic。©2019日本电气工程师学会。约翰·威利父子出版公司。
When an electroencephalogram (EEG) is measured, artifacts due to eye blinks and eye movements contaminate the EEG signal. One of the solutions applied to this problem is independent component analysis (ICA), which can separate the artifact components from EEG data. However, the identification of independent components (ICs) related to artifacts, particularly in the case of multiple noise signals from eye blinks and movements, has not been investigated fully. The purpose of this work is to identify ICs related to eye‐blink and eye‐movement artifacts in EEG data. In the proposed method, an artificial EEG with 28 channels was first created by superimposing eye‐blink and eye‐movement templates, and then ICA was applied to the artificial EEG to obtain ICs. Afterward, feature amounts of kurtosis for each IC and the cross‐correlation coefficient between each IC and the eye‐blink template and eye‐movement template were extracted, and clustering was performed by K‐means clustering. As a result, the most reasonable ICs related to multiple artifacts were identified in comparison with the results of round‐robin ICs. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.