[1]赵丽,崔立杰.基于变分模态分解的眼电伪迹去除[J].中国医学物理学杂志,2020,37(2):237-242.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.018]
 ZHAO Li,CUI Lijie.Electrooculogram artifacts removal based on variational mode decomposition[J].Chinese Journal of Medical Physics,2020,37(2):237-242.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.018]
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基于变分模态分解的眼电伪迹去除()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
37
期数:
2020年第2期
页码:
237-242
栏目:
医学信号处理与医学仪器
出版日期:
2020-03-03

文章信息/Info

Title:
Electrooculogram artifacts removal based on variational mode decomposition
文章编号:
1005-202X(2020)02-0237-06
作者:
赵丽崔立杰
天津职业技术师范大学自动化与电气工程学院, 天津 300222
Author(s):
ZHAO Li CUI Lijie
School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
关键词:
眼电伪迹脑电信号变分模态分解模态分量
Keywords:
Keywords: electrooculogram artifact electroencephalogram signal variational mode decomposition modal component
分类号:
R318;TN911.71
DOI:
DOI:10.3969/j.issn.1005-202X.2020.02.018
文献标志码:
A
摘要:
脑电信号可以反映人体大脑活动状态,精确地将脑内信息传递向外界,对脑科学研究具有重要的意义。在实际情况中,脑电信号采集的同时会带有一些噪声,而眼电伪迹的存在会严重干扰脑电信号。本研究尝试了一种基于变分模态分解的眼电伪迹去除方法。通过变分模态分解将采集到的脑电信号分解成K组模态分量;根据眼电伪迹的频率特点,选择出眼电伪迹所对应的模态分量,并将其去除后重新构建剩余的模态分量。结果表明通过对实验数据的处理,变分模态分解可以有效地将眼电伪迹去除,并维持脑电信号的特征。
Abstract:
Abstract: Electroencephalogram (EEG) signals can reflect the state of human brain activity and accurately transmit the information in the brain to the outside, which is of great significance to brain science research. In practice, the acquisition of EEG signals is accompanied by some noises, and the presence of electrooculogram (EOG) artifacts will seriously interfere with EEG signals. Therefore, a variational mode decomposition-based method for removing EOG artifacts is proposed. The acquired EEG signals are decomposed into K modal components by variational mode decomposition. According to the frequency characteristics of EOG artifacts, the modal components corresponding to EOG artifacts are selected out, and the remaining modal components are reconstructed after removing the artifacts. The results show that by processing the experimental data, variational mode decomposition can be used to effectively remove EOG artifacts and maintain the characteristics of EEG signals.

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备注/Memo

备注/Memo:
【收稿日期】2019-10-26 【基金项目】天津市自然科学基金(18JCYBJC88200) 【作者简介】赵丽,教授,硕士生导师,研究方向:生物医学信号处理、智能信息检测,E-mail: zheng862080@139.com
更新日期/Last Update: 2020-03-03