单通道脑电信号眼电伪迹去除算法研究

刘志勇; 孙金玮; 卜宪庚

doi:10.16383/j.aas.2017.c160191

单通道脑电信号眼电伪迹去除算法研究

doi: 10.16383/j.aas.2017.c160191

刘志勇^1,2,,
孙金玮^1, ,,
卜宪庚^3,

1.
哈尔滨工业大学电气工程及自动化学院哈尔滨 150001
2.
61135 部队北京 102211
3.
哈尔滨医科大学基础医学院哈尔滨 150081

基金项目:

哈尔滨市科技创新人才研究专项资金 2015RA XXJ038

中央高校基本科研业务费专项资金 2013004

国家自然科学基金 61301012

中央高校基本科研业务费专项资金 2013005

详细信息

作者简介:
刘志勇哈尔滨工业大学电气工程及自动化学院博士研究生.2008年获得哈尔滨工业大学电气工程及自动化学院学士学位.主要研究方向为生物医学信号处理.E-mail:liuzhiyong563@hit.edu.cn

卜宪庚哈尔滨医科大学基础医学院教授.主要研究方向为物联网及其应用.E-mail:bxg@ems.hrbmu.edu.cn

通讯作者:
孙金玮哈尔滨工业大学电气工程及自动化学院教授.主要研究方向为生物医学传感器, 主动噪声控制理论.本文通信作者, E-mail:jwsun@hit.edu.cn

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- 文章访问数: 2331
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出版历程
- 收稿日期: 2016-03-09
- 录用日期: 2016-07-18
- 刊出日期: 2017-10-20

EOG Artifact Removing Method for Single-channel EEG Signal

LIU Zhi-Yong^{1,2
,},
SUN Jin-Wei^{1
, ,},
BU Xian-Geng^3
,

1.
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001
2.
61135 PLA Troop, Beijing 102211
3.
School of Basic Medical, Harbin Medical University, Harbin 150081

Funds:

Sci-tech Innovation Foundation of Harbin 2015RA XXJ038

Fundamental Research Funds for the Central Universities 2013004

National Natural Science Foundation of China 61301012

Fundamental Research Funds for the Central Universities 2013005

More Information

Author Bio:
Ph. D. candidate at the School of Electrical Engineering and Automation, Harbin Institute of Technology. He received his bachelor degree from Harbin Institute of Technology in 2008. His main research interest is biomedical signal processing

Professor at the School of Basic Medical, Harbin Medical University. His research interest covers internet of things and its application

Corresponding author: SUN Jin-Wei Professor at the School of Electrical Engineering and Automation, Harbin Institute of Technology. His research interest covers biomedical sensors and active noise control theory. Corresponding author of this paper, E-mail:jwsun@hit.edu.cn

摘要

摘要: 由眨眼和眼动产生的眼电伪迹（Electrooculography，EOG）信号是脑电信号（Electroencephalography，EEG）中的主要噪声信号之一.目前，多通道脑电信号中眼电伪迹的去除算法已经较为成熟.而在单通道脑电信号的眼电伪迹去除中，由于采集通道数量较少且缺乏参考眼电信号，目前尚无十分有效的去除方法.本文提出一种基于小波变换（Wavelet transform，WT）、集合经验模态分解（Ensemble empirical mode decomposition，EEMD）和独立成分分析（Independent component analysis，ICA）的WT-EEMD-ICA单通道脑电信号眼电伪迹去除算法.实验表明：WT-EEMD-ICA算法有效地解决了单通道WT-ICA算法中的超完备问题，能够有效去除单通道脑电信号中的眼电伪迹，并且分离出的眼电伪迹成分与参考通道采集的眼电信号相关性较强.
- 脑电信号 /
- 眼电伪迹 /
- 小波变换 /
- 集合经验模态分解 /
- 独立成分分析
Abstract: Electrooculography (EOG) artifacts generated by eye movements and blinks are the major artifacts in electroencephalography (EEG). There are many common effective methods for removing the multi-channel EEG artifacts. However, due to the limitation of input channel number and the lack of reference EOG signal, there is no very effective artifact removing method for single-channel EEG signal. In the present study, a novel EOG artifact removing algorithm WT-EEMD-ICA for single-channel EEG signal is proposed based on wavelet transform (WT), ensemble empirical mode decomposition (EEMD) and independent component analysis (ICA) technologies. The result shows that the WT-EEMDICA method, which successfully solves the overcomplete problem of WT-ICA in single channel artifact removal, can separate the EOG and EEG successfully only from one single-channel EEG, and that the useful information involved in original EEG signal can be greatly saved.
- Electroencephalography(EEG) /
- electrooculography(EOG) /
- wavelet transform(WT) /
- ensemble empirical mode decomposition(EEMD) /
- independent component analysis(ICA)
注释:

1) 本文责任编委田捷

HTML全文

图 1 WT-EEMD-ICA算法框图

Fig. 1 The block of WT-EEMD-ICA algorithm

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图 2 实验设计框图

Fig. 2 The block of experiment design

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图 3 EEG$_{\rm ac}$和EOG$_{\rm ac}$波形

Fig. 3 Wave of EEG$_{\rm ac}$和EOG$_{\rm ac}$

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图 4 $P1$段EEG$_{\rm ac}$和EOG$_{\rm ac}$波形

Fig. 4 Wave of EEG$_{\rm ac}$和EOG$_{\rm ac}$ in $P1$

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图 5 带通滤波前后EEG与EOG信号对比

Fig. 5 EEG and EOG processed by band pass filter

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图 6 带阻滤波前后EEG与EOG信号对比

Fig. 6 EEG and EOG processed by band stop filter

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图 7 EEG信号小波分解图

Fig. 7 Wavelet coefficients of EEG

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图 8 $d4$小波系数EEMD结果图

Fig. 8 The EEMD results of $d4$

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图 9 $d4$小波系数ICA结果图

Fig. 9 The ICA results of $d4$

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图 10 $d4$小波系数WT-EEMD-ICA结果

Fig. 10 The WT-EEMD-ICA results of $d4$

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图 11 $d3$小波系数WT-EEMD-ICA结果

Fig. 11 The WT-EEMD-ICA results of $d3$

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图 12 $P1$段信号WT-EEMD-ICA结果

Fig. 12 The WT-EEMD-ICA results of signals in $P1$

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图 13 $P2$段信号WT-EEMD-ICA结果

Fig. 13 The WT-EEMD-ICA results of signals in $P2$

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图 14 $P1+P2$段信号WT-EEMD-ICA结果

Fig. 14 The WT-EEMD-ICA results of signals in $P1+P2$

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图 15 WT-EEMD-ICA算法稳定性实验结果

Fig. 15 Stability results of WT-EEMD-ICA

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图 16 $P1$段信号WT算法结果

Fig. 16 The WT algorithm results of signals in $P1$

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图 17 $P1$段信号WT-ICA算法结果

Fig. 17 The WT-ICA algorithm results of signals in $P1$

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图 18 $P1$段信号EEMD-ICA算法结果

Fig. 18 The EEMD-ICA algorithm results of signals in $P1$

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图 19 $P1$段信号DE-ICA算法结果

Fig. 19 The DE-ICA algorithm results of signals in $P1$

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图 20 $P1$段信号HHT算法结果

Fig. 20 The HHT algorithm results of signals in $P1$

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表 1 脑电分量与眼电分量近似熵对比

Table 1 Comparison of $ApEn$ between EEG and EOG

$ApEn$ 脑电分量眼电分量

均值 1.35 0.55

标准差 0.26 0.23

下载: 导出CSV

表 2 重构信号与原始信号相关系数

Table 2 The correlation coefficients between reconstructed and original signal

对比算法 $S1$ $S2$ EOG

WT-EEMDICA 0.82 0.74 0.54

WT 0.62 0.67 0.56

WT-ICA 0.54 0.59 0.57

EEMD-ICA 0.70 0.59 0.31

HHT 0.64 0.57 0.32

下载: 导出CSV

参考文献(26)

[1]	Marker R J, Maluf K S. Effects of electrocardiography contamination and comparison of ECG removal methods on upper trapezius electromyography recordings. Journal of Electromyography and Kinesiology, 2014, 24(6):902-909 doi: 10.1016/j.jelekin.2014.08.003
[2]	胡进, 侯增广, 陈翼雄, 张峰, 王卫群.下肢康复机器人及其交互控制方法.自动化学报, 2014, 40(11):2377-2390 http://www.aas.net.cn/CN/abstract/abstract18514.shtml Hu Jin, Hou Zeng-Guang, Chen Yi-Xiong, Zhang Feng, Wang Wei-Qun. Lower limb rehabilitation robots and interactive control methods. Acta Automatica Sinica, 2014, 40(11):2377-2390 http://www.aas.net.cn/CN/abstract/abstract18514.shtml
[3]	Zeng H, Song A G, Yan R Q, Qin H Y. EOG artifact correction from EEG recording using stationary subspace analysis and empirical mode decomposition. Sensors, 2013, 13(11):14839-14859 doi: 10.3390/s131114839
[4]	王行愚, 金晶, 张宇, 王蓓.脑控:基于脑-机接口的人机融合控制.自动化学报, 2013, 39(3):208-221 http://www.aas.net.cn/CN/abstract/abstract17800.shtml Wang Xing-Yu, Jin Jing, Zhang Yu, Wang Bei. Brain control:human-computer integration control based on braincomputer interface. Acta Automatica Sinica, 2013, 39(3):208-221 http://www.aas.net.cn/CN/abstract/abstract17800.shtml
[5]	Jie D, Tao W, Zhang A T, Dai H Y. The removal of blink and saccade artifact in EEG recordings by independent component analysis. In:Proceedings of the 3rd International Conference on Biomedical Engineering and Informatics. Yantai, China:IEEE, 2010, 3:1071-1075 http://ieeexplore.ieee.org/xpl/tocresult.jsp?sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A5639235%29&pageNumber=3
[6]	Daly I, Scherer R, Billinger M, Müller-Putz G. FORCe:fully online and automated artifact removal for brain-computer interfacing. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2015, 23(5):725-736 doi: 10.1109/TNSRE.2014.2346621
[7]	Mannan M M N, Jeong M Y, Kamran M A. Hybrid ICAregression:automatic identification and removal of ocular artifacts from electroencephalographic signals. Frontiers in Human Neuroscience, 2016, 10:193-193 https://core.ac.uk/display/43879890
[8]	Qi W. EOG artifacts removal in EEG measurements for affective interaction with brain computer interface. In:Proceedings of the 8th International Conference on Intelligent Information Hiding and Multimedia Signal Processing. Piraeus-Athens, Greece:IEEE, 2012. 471-475
[9]	Chang W D, Cha H S, Kim K, Im C H. Detection of eye blink artifacts from single prefrontal channel electroencephalogram. Computer Methods and Programs in Biomedicine, 2016, 124:19-30 doi: 10.1016/j.cmpb.2015.10.011
[10]	Vigon L, Saatchi M R, Mayhew J E W, Fernandes R. Quantitative evaluation of techniques for ocular artefact filtering of EEG waveforms. IEE Proceedings-Science Measurement and Technology, 2000, 147(5):219-229 doi: 10.1049/ip-smt:20000475
[11]	Fatourechi M, Bashashatia A, Ward R K, Birch G E. EMG and EOG artifacts in brain computer interface systems:a survey. Clinical Neurophysiology, 2007, 118(3):480-494 doi: 10.1016/j.clinph.2006.10.019
[12]	Gomez-Herrero G, Clercq W D, Anwar H, Kara O, Egiazarian K, van Huffel S, van Paesschen W. Automatic removal of ocular artifacts in the EEG without an EOG reference channel. In:Proceedings of the 7th Nordic Signal Processing Symposium. Reykjavik, Iceland:IEEE, 2006. 130-133
[13]	陈永强, 王宏霞.基于新型混合模型的欠定盲分离方法.自动化学报, 2014, 40(7):1412-1420 http://www.aas.net.cn/CN/abstract/abstract18412.shtml Chen Yong-Qiang, Wang Hong-Xia. A method for underdetermined blind source separation based on new mixture model. Acta Automatica Sinica, 2014, 40(7):1412-1420 http://www.aas.net.cn/CN/abstract/abstract18412.shtml
[14]	Jung T P, Makeig S, Westerfield M, Townsend J, Courchesne E, Sejnowski T J. Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clinical Neurophysiology, 2000, 111(10):1745-1758 doi: 10.1016/S1388-2457(00)00386-2
[15]	del R Millan J, Mourino J, Franze M, Cincotti F, Varsta M, Heikkonen J, Babiloni F. A local neural classifier for the recognition of EEG patterns associated to mental tasks. IEEE Transactions on Neural Networks, 2002, 13(3):678-686 doi: 10.1109/TNN.2002.1000132
[16]	Croft R J, Barry R J. Removal of ocular artifact from the EEG:a review. Neurophysiologie Clinique, 2000, 30(1):5-19 doi: 10.1016/S0987-7053(00)00055-1
[17]	Zhou W D, Gotman J. Removing eye-movement artifacts from the EEG during the intracarotid amobarbital procedure. Epilepsia, 2005, 46(3):409-414 doi: 10.1111/epi.2005.46.issue-3
[18]	Sarossy M G, Lee M H A, Bach M. A fast automated method for calculating the EOG arden ratio. Documenta Ophthalmologica, 2014, 128(3):169-178 doi: 10.1007/s10633-014-9430-5
[19]	吴明权, 李海峰, 马琳.单通道脑电信号中眼电干扰的自动分离方法.电子与信息学报, 2015, 37(2):367-372 doi: 10.11999/JEIT140602 Wu Ming-Quan, Li Hai-Feng, Ma Lin. Automatic electrooculogram separation method for single channel electroencephalogram signals. Journal of Electronics and Information Technology, 2015, 37(2):367-372 doi: 10.11999/JEIT140602
[20]	James C J, Lowe D. Single channel analysis of electromagnetic brain signals through ICA in a dynamical systems framework. In:Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Istanbul, Turkey:IEEE, 2001. 1974-1977
[21]	Davies M E, James C J. Source separation using single channel ICA. Signal Processing, 2007, 87(8):1819-1832 doi: 10.1016/j.sigpro.2007.01.011
[22]	Mammone N, La Foresta F, Morabito F C. Automatic artifact rejection from multichannel scalp EEG by wavelet ICA. IEEE Sensors Journal, 2012, 12(3):533-542 doi: 10.1109/JSEN.2011.2115236
[23]	Wu Z H, Huang N E. Ensemble empirical mode decomposition:a noise-assisted data analysis method. Advances in Adaptive Data Analysis, 2009, 1(1):1-41 doi: 10.1142/S1793536909000047
[24]	Tang Y, Li J P, Wu H. A simple and accurate ICA algorithm for separating mixtures of up to four independent components. Acta Automatica Sinica, 2011, 37(7):794-799 doi: 10.1016/S1874-1029(11)60210-3
[25]	Li H Q, Feng X L, Cao L, Li E B, Liang H A, Chen X L. A new ECG signal classification based on WPD and ApEn feature extraction. Circuits Systems and Processing, 2016, 35(1):339-352 doi: 10.1007/s00034-015-0068-7
[26]	Li M A, Yang L B, Yang J F. Separation of EOG artifacts from EEG signals using Hilbert-Huang transform. In:Proceedings of the 2011 International Conference on Electric Information and Control Engineering. Wuhan, China:IEEE, 2011. 4453-4456

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单通道脑电信号眼电伪迹去除算法研究

doi: 10.16383/j.aas.2017.c160191

通讯作者:
孙金玮哈尔滨工业大学电气工程及自动化学院教授.主要研究方向为生物医学传感器, 主动噪声控制理论.本文通信作者, E-mail:jwsun@hit.edu.cn

计量

EOG Artifact Removing Method for Single-channel EEG Signal

Corresponding author: SUN Jin-Wei Professor at the School of Electrical Engineering and Automation, Harbin Institute of Technology. His research interest covers biomedical sensors and active noise control theory. Corresponding author of this paper, E-mail:jwsun@hit.edu.cn

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目录

$ApEn$	脑电分量	眼电分量
均值	1.35	0.55
标准差	0.26	0.23

对比算法	$S1$	$S2$	EOG
WT-EEMDICA	0.82	0.74	0.54
WT	0.62	0.67	0.56
WT-ICA	0.54	0.59	0.57
EEMD-ICA	0.70	0.59	0.31
HHT	0.64	0.57	0.32

留言板

单通道脑电信号眼电伪迹去除算法研究

doi: 10.16383/j.aas.2017.c160191

通讯作者: 孙金玮 哈尔滨工业大学电气工程及自动化学院教授.主要研究方向为生物医学传感器, 主动噪声控制理论.本文通信作者, E-mail:jwsun@hit.edu.cn

计量

出版历程

EOG Artifact Removing Method for Single-channel EEG Signal

Corresponding author: SUN Jin-Wei Professor at the School of Electrical Engineering and Automation, Harbin Institute of Technology. His research interest covers biomedical sensors and active noise control theory. Corresponding author of this paper, E-mail:jwsun@hit.edu.cn

计量

出版历程

目录

通讯作者:
孙金玮哈尔滨工业大学电气工程及自动化学院教授.主要研究方向为生物医学传感器, 主动噪声控制理论.本文通信作者, E-mail:jwsun@hit.edu.cn