眼电伪迹自动识别与去除的新方法

doi:10.3969/j.issn.0372-2112.2016.05.004

电子学报 ›› 2016, Vol. 44 ›› Issue (5): 1032-1039.DOI: 10.3969/j.issn.0372-2112.2016.05.004

眼电伪迹自动识别与去除的新方法

李明爱^1,2, 郭硕达¹, 田晓霞¹, 杨金福^1,2, 郝冬梅³

1. 北京工业大学电子信息与控制工程学院, 北京 100124;
2. 计算智能与智能系统北京市重点实验室, 北京 100124;
3. 北京工业大学生命科学与生物工程学院, 北京 100124

收稿日期:2014-08-14 修回日期:2015-05-05 出版日期:2016-05-25
- 通讯作者:
- 李明爱
- 作者简介:
- 郭硕达 男,2012年于武汉纺织大学获得学士学位,现为北京工业大学控制科学与工程专业硕士研究生,主要研究方向为脑机接口技术、信息处理与模式识别.
- 基金资助:
- 国家自然科学基金 (No.81471770,No.61201362); 北京市自然科学基金 (No.7132021,No.7132028)

A Novel Automatic Recognition and Removal Method of Ocular Artifacts

LI Ming-ai^1,2, GUO Shuo-da¹, TIAN Xiao-xia¹, YANG Jin-fu^1,2, HAO Dong-mei³

1. College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China;
2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China;
3. College of Life Science and Bio-Engineering, Beijing 100124, China

Received:2014-08-14 Revised:2015-05-05 Online:2016-05-25 Published:2016-05-25

摘要/Abstract

摘要：

为了改善脑电中的眼电伪迹过估计问题及环境干扰耦合引起的非线性混合对眼电去除效果的影响,提出一种基于快速核独立成分分析(Fast Kernel Independent Component Analysis,FastKICA)与离散小波变换(Discrete Wavelet Transform,DWT)的眼电自动去除方法,即(Fast Kernel Independent Wavelet Transform ,FKIWT)方法.首先,利用FastKICA方法对脑电信号进行分离得到独立成分,并以相关系数为依据识别出眼电伪迹;进而,基于DWT对眼电伪迹进行多分辨率分析,将逼近分量置零,而细节分量保持不变,使得重构所得眼电伪迹成分保留更多有用脑电信号;最后,利用FastKICA逆变换重建眼电去除后的脑电信号.实验结果表明:FKIWT不仅有效改善了眼电过估计问题,增强了抗干扰能力和鲁棒性,而且在线性混合和非线性混合情况下,均得到较好的伪迹去除效果,特别是在非线性混合时优势更为明显,适合于实际在线应用.

关键词: 非线性混合模型, 快速核独立成分分析, 离散小波变换, 眼电过估计, 鲁棒性

Abstract:

In order to improve the overestimation of ocular artifacts (OA) in electroencephalogram (EEG) and the OA removal effect of nonlinear mixture caused by environmental interference coupling, a novel automatic removal method is proposed based on fast kernel independent component analysis (FastKICA) and discrete wavelet transform (DWT), and it is denoted as FKIWT.The independent components are separated from the mixed EEG by using the FastKICA algorithm, and the correlation coefficient is applied to identify OA component;Then, the Multiresolution analysis of OA is achieved with DWT, the approximation wavelet coefficients are set to zero and the detail wavelet coefficients are not changed.So more useful EEG is remained in the reconstructed OA component;Furthermore, the clean EEG is restored with the inverse algorithm of FastKICA.The experimental results show that FKIWT can effectively improve the overestimation of OA and has perfect anti-interference ability and robustness.Meanwhile, the better effects of OA elimination are also obtained on the condition that the linear or nonlinear mixed model is adopted, and the latter's advantage is especially obvious.The FKIWT is suitable for on-line application.

Key words: nonlinear mixed model, fast kernel independent component analysis, discrete wavelet transform, overestimation of ocular artifacts, robustness

中图分类号:

R318

李明爱, 郭硕达, 田晓霞, 杨金福, 郝冬梅. 眼电伪迹自动识别与去除的新方法[J]. 电子学报, 2016, 44(5): 1032-1039.

LI Ming-ai, GUO Shuo-da, TIAN Xiao-xia, YANG Jin-fu, HAO Dong-mei. A Novel Automatic Recognition and Removal Method of Ocular Artifacts[J]. Acta Electronica Sinica, 2016, 44(5): 1032-1039.

参考文献

[1] Akhtar M T,Mitsuhashi W,James C J.Employing spatially constrained ICA and wavelet denoising,for automatic removal of artifacts from multichannel EEG data[J].Signal Processing,2012,92(2):401-416.
[2] Vijila C,et al.Artifacts removal in EEG signal using adaptive neuro fuzzy inference system[A].2007 International Conference of Signal Processing,Communications and Networking[C].Chennai:IEEE,2006.589-591.
[3] Jung T P,Makeig S,Humphries C,et al.Removing electroencephalographic artifacts by blind source separation[J].Psychophysiology,2000,37(2):163-178.
[4] Cardoso J F,Souloumiac A.Blind beamforming for non-Gaussian signals[J].IEE Proceedings-F Padar and Signal Processing,1993,140(6):362-370.
[5] Pham D T,Cardoso J F.Blind separation of instantaneous mixtures of nonstationary sources[J].IEEE Transactions on Signal Processing,2001,49(9):1837-1848.
[6] Karvanen J,Koivunen V.Blind separation methods based on Pearson system and its extensions[J].Signal Processing,2002,82(4):663-673.
[7] Shen H,Kleinsteuber M,Huper K.Local convergence analysis of FastICA and related algorithms[J].IEEE Transactions on Neural Networks,2008,19(6):1022-1032.
[8] Dermoune A,Wei T W.FastICA algorithm:Five criteria for the optimal choice of the nonlinearity function[J].IEEE Transactions on Signal Processing,2013,61(8):2078-2087.
[9] 李明爱,崔燕,杨金福.脑电信号中眼电伪迹自动去除方法的研究[J].电子学报,2013,41(06):1207-1213. LI Ming-ai,CUI Yan,YANG Jin-fu.Research on removing ocular artifact automatically from EEG signals[J].Acta Electronica Sinica,2013,41(06):1207-1213.(in Chinese)
[10] Klados M A,Papadelis C L,Bamidis P D.REG-ICA:A new hybrid method for EOG artifact rejection[A].20099^th International Conference on Information Technology and Applications in Biomedicine[C].Larnaka:IEEE,2009.278-281.
[11] 李晓欧.基于独立分量分析和共同空间模式的脑电特征提取方法[J].生物医学工程学杂志,2010,27(06):1370-1374. LI Xiao-ou.EEG feature extraction basedon ICA and CSP algorithms[J].Journal of Biomedical Engineering,2010,27(06):1370-1374.(in Chinese)
[12] 万柏坤,等.ICA去除EEG中眼动伪差和工频干扰方法研究[J].电子学报,2003,31(10):1571-1574. WANG Bai-kun,ZHU Xin,YANG Chun-mei,et al.Study on applying independent component analysis to remove blink artifacts and power noise in EEG[J].Acta Electronica Sinica,2003,31(10):1571-1574.(in Chinese)
[13] Marchesi S,Bruzzone L.ICA and kernel ICA for change detection in multispectral remote sensing images[A].2009 IEEE International Geoscience and Remote Sensing Symposium[C].Cape Town:IEEE,2009.1231-1234.
[14] Tateyama T,Nakao Z,Chen Y W.Classification of brain matters in MRI by kernel independent component analysis[A].20084^th International Conference on Intelligent Information Hiding and Multimedia Signal Processing Proceedings[C].Harbin:IEEE Computer Soc,2008.713-716.
[15] Shen H,Jegelka S,Gretton A.Fast kernel-based independent component analysis[J].IEEE Transactions on Signal Processing,2009,57(9):3498-3511.
[16] 罗志增,等.基于小波变换和盲信号分离的多通道肌电信号处理方法[J].电子学报,2009,37(04):823-827. LUO Zhi-zeng,et al.A method of multi-channel EMG disposal based on wavelet transform and blind signal separation[J].Acta Electronica Sinica,2009,37(04):823-827.(in Chinese)
[17] 王明祥,宁宇蓉,王晋国.基于Mallat算法的一维离散小波变换的实现[J].西北大学学报(自然科学版),2006,36(03):364-368. WANG Ming-xiang,NING Yu-rong,WANG Jin-guo.Realization of one dimension discrete wavelet transform based on Mallat algorithm[J].Journal of Northwest University(Natural Science Edition),2006,36(03):364-368.(in Chinese)

眼电伪迹自动识别与去除的新方法

A Novel Automatic Recognition and Removal Method of Ocular Artifacts

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