基于DIVA模型的脑电信号去噪方法研究

doi:10.3969/j.issn.0372-2112.2015.04.011

电子学报 ›› 2015, Vol. 43 ›› Issue (4): 700-707.DOI: 10.3969/j.issn.0372-2112.2015.04.011

基于DIVA模型的脑电信号去噪方法研究

张少白¹, 王勇¹, 刘友谊²

1. 南京邮电大学计算机学院, 江苏南京 210003;
2. 北京师范大学认知神经科学与学习国家重点实验室, 北京 100875

收稿日期:2014-08-28 修回日期:2014-12-01 出版日期:2015-04-25
- 作者简介:
- 张少白 男,1953年生于湖北武汉,博士研究生学历,南京邮电大学计算机学院教授.研究方向为模式识别与智能系统、智能信息处理以及计算机应用等.E-mail:adzsb@163.com;王勇男,1990年生,硕士研究生,研究方向:模式识别与智能系统.
- 基金资助:
- 国家自然科学基金 (No.61373065,No.61271334)

Research on the Method of EEG Signal Denoising Based on the DIVA Model

ZHANG Shao-bai¹, WANG Yong¹, LIU You-yi²

1. Computer Department, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210003, China;
2. State Key Laboratory for Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China

Received:2014-08-28 Revised:2014-12-01 Online:2015-04-25 Published:2015-04-25
- Supported by:
- National Natural Science Foundation of China (No.61373065, No.61271334)

摘要/Abstract

摘要：

脑电信号获取过程中,工频噪声干扰现象往往会使所获取的信息产生多种多形态瞬时结构波形,这种现象影响到DIVA(Directions Into Velocities of Articulators)模型对语音的正常处理.为此,本文提出了一种面向特征提取的脑电信号结构自适应稀疏分解模型,并在此基础上,通过采用匹配追踪算法求解最佳原子、使用过完备原子库中原子表示原始脑电信号等方法,实现了信号去噪的目的,效果好于传统的小波变换去噪方法.仿真实验表明,本文提出的方法提高了DIVA模型语音发音的精度.

关键词: DIVA模型, 脑电信号, 噪声, 稀疏分解

Abstract:

There are power frequency interference and other kinds of noise in the electro encephalo gram (EEG)signal acquisition process.They make the signal show non-stationary and a variety of multi-form waveform in the instantaneous structure.Then such signal will affect the normal processing of the speech in DIVA(Directions Into Velocities of Articulators) model.Therefore,this paper proposes an adaptive sparse decomposition model for the feature extraction of EEG signal structure and makes use of Matching Pursuit algorithm to solve the optimal atom.Then the original EEG signal can be represented by atoms in the complete atomic library.Finally,this model removes noise that exists in the EEG signal and is compared with wavelet transform method.Simulation results show that after we put the denoising EEG signal into the model,the phonetic pronunciation improves.

Key words: directions into velocities of articulators (DIVA) model, electro encephalo gram (EEG) signal, noise, sparse decomposition

中图分类号:

TP183

张少白, 王勇, 刘友谊. 基于DIVA模型的脑电信号去噪方法研究[J]. 电子学报, 2015, 43(4): 700-707.

ZHANG Shao-bai, WANG Yong, LIU You-yi . Research on the Method of EEG Signal Denoising Based on the DIVA Model[J]. Acta Electronica Sinica, 2015, 43(4): 700-707.

参考文献

[1] Guenther FH,Brumberg JS,Wright EJ,Nieto Castanon.A wireless brain-machine interface for real-time speech synthesis[J].PLoS ONE,2009,4(12):e8218.
[2] Brumberg JS,Nieto Castanon A,Kennedy PR,Guenther FH.Brain-computer interfaces for speech communication[J].Speech Communication,2010,52(4):367-379.
[3] Tourville JT,Guenther FH. The DIVA model:a neural theory of speech acquisition and production[J].Language and Cognitive Processes,2011,25(7):952-981.
[4] 杜晓燕,等.脑电信号伪迹去除的研究进展[J].生物医学工程学杂志,2008,25(2):464-471. Du Xiaoyan,et al.Removal of artifacts from EEG signal[J].Journal of Biomedical Engineering,2008,25(2):464-471.(in Chinese)
[5] 于霞,刘建昌,李鸿儒.一种变步长凸组合自适应滤波器及其均方性能分析[J].电子学报,2010,38(2):480-484. Yu Xia,Liu Jian-chang,Li Hong-ru.A convex combination of variable step-size adaptive filter and its mean-square performance analysis[J].Acta Electronica Sinica,2010,38(2):480-484.(in Chinese)
[6] 徐宝国,宋爱国,费树岷.在线脑机接口中脑电信号的特征提取与分类方法[J].电子学报,2011,38(5):1025-1035. Xu Bao-guo,Song Ai-guo,Fei Shu-min.Feature extraction and classification of EEG in online brain-computer interface[J].Acta Electronica Sinica,2011,38(5):1025-1035.(in Chinese)
[7] Stepphanc Mallat,Zhifeng Zhang.Matching pursuit with time-frequency dictionaries dictionaries[J].IEEE Trans on Signal Processing,1993,41(12):3397-3415.
[8] Chen S,Donoho D,Saunders M.Atomic decomposition by basis pursuit[J].SIAM Journal on Scientific Computing,1998,20:33-61.
[9] Shaobai zhang,Xin zhang.An improved phonetic learning algorithm based on the DIVA model[J].Lecture Notes in Electrical Engineering,2011,123(12):495-500.
[10] Shaobai zhang,Liqin gao.Application of feedforward and feedback control strategy in the speech acquisition and production model[J].Lecture Notes in Electrical Engineering,2011,123(2):489-494.
[11] Guenther FH.Cortical interactions underlying the production of speech sounds[J].Journal of Communication Disorders,2006,39(5):350-365.
[12] Bohland JW,Guenther FH.An fMRI investigation of syllable sequence production[J].NeuroImage,2006,32(2):821-841.
[13] Coifman R,Wickerhauser M.Entropy-based algorithms for best basis selection[J].IEEE Transactions on Information Theory,1992,38:713-718.
[14] Cai T Tony,Wang Lie.Orthogonal matching pursuit for sparse signal recovery with noise[J].IEEE Transactions on Information Theory,2011,57(7):4680-4688.
[15] 张春梅,尹忠科,肖明霞.基于冗余字典的信号过完备表示与稀疏分解[J].科学通报,2006,51(6):628-633. Zhang chunmei,Yin zhongke,Xiao Mingxia.Over complete representation and sparse decomposition of signal based on the redundant dictionary had[J].Chinese Science Bulletin,2006,51(6):628-633.(in Chinese)
[16] Davis G,Mallat S,Avellaneda M.Adaptive greedy approximation[J].Constr Approx,1997,13(1):57-98.
[17] Donoho,X Huo.Uncertainty principles and ideal atomic decomposition[J].IEEE Trans Information Theory,2001,47(7):2845-2862.
[18] 吴敏.基于稀疏表示模型的EEG信号棘波自检测技术与应用系统研究[D].江苏南京:南京理工大学,2010.8. Wu min.Automatic detection of epileptic characteristics in EEG signals based on sparse representation and the design of an application system[D].Nanjing,Jiangsu:Nanjing University of Science and Technology,2010.8.(in Chinese)
[19] Mor Etai,Azoulay Amnon,Aladjem Mayer.A matching pursuit method for approximating over lapping ultrasonic echoes[J].IEEE Trans Ultrason Ferroelectr Freq Control,2010,57(9):1996-2004.

基于DIVA模型的脑电信号去噪方法研究

Research on the Method of EEG Signal Denoising Based on the DIVA Model

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