基于CEEMDAN-ICA的单通道脑电信号眼电伪迹滤除方法
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摘要
传统盲源分离法不能解决欠定问题,且分离信号与源信号对应关系不确定。提出一种基于自适应噪声完备经验模态分解(CEEMDAN)和独立成分分析(ICA)相结合的脑电信号眼电伪迹自动去除方法。该方法首先将含伪迹脑电信号自适应分解成多维本征模态函数(IMF),以满足盲源分离方法对信号正定或超定要求,再对本征模态函数用ICA方法构建多维源信号,最后利用模糊熵阈值判据判别多维源信号中的伪迹信号,完成滤波并重构脑电信号。该方法相比于其他算法,能更好的去除眼电伪迹并保留原始信息,适合单通道脑电信号预处理。
The traditional blind source separation method can not solve the underdetermined problem,and the corresponding relationship between the separated signal and the source signal is uncertain. An automatic removal method of EEG artifacts based on the combination of complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) and independent component analysis(ICA) is proposed. This method firstly decomposes EEG signals containing artifacts into multi-dimensional intrinsic mode functions adaptively to meet the requirements of positive or overdetermined signals in blind source separation model,and then builds multi-dimensional source signals by using ICA method for intrinsic mode functions. Finally,we use the threshold criterion based on fuzzy entropy to filter the artifact signals in the multi-dimensional source signal and reconstruct the EEG signal. Compared with other algorithms,this method can better remove the eye artifacts and retain the original information,which is suitable for single-channel EEG signal preprocessing.
The traditional blind source separation method can not solve the underdetermined problem,and the corresponding relationship between the separated signal and the source signal is uncertain. An automatic removal method of EEG artifacts based on the combination of complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) and independent component analysis(ICA) is proposed. This method firstly decomposes EEG signals containing artifacts into multi-dimensional intrinsic mode functions adaptively to meet the requirements of positive or overdetermined signals in blind source separation model,and then builds multi-dimensional source signals by using ICA method for intrinsic mode functions. Finally,we use the threshold criterion based on fuzzy entropy to filter the artifact signals in the multi-dimensional source signal and reconstruct the EEG signal. Compared with other algorithms,this method can better remove the eye artifacts and retain the original information,which is suitable for single-channel EEG signal preprocessing.
引文
[1]马颖颖,张泾周,吴疆.脑电信号处理方法[J].北京生物医学工程,2007(1):99-102.
[2]马玉良,许明珍,佘青山,等.基于自适应阈值的脑电信号去噪方法[J].传感技术学报,2014,27(10):1368-1372.
[3]Tatum W O,Dworetzky B A,Schomer D L.Artifact and Recording Concepts in EEG[J].Journal of Clinical Neurophysiology,2011,28(3):252-263.
[4]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[J].电子与信息学报,2015,37,27(2):367-372.
[5]Vigon L,Saatchi M R,Mayhew J E W,et al.Quantitative Evaluation of Techniques for Ocular Artefact Filtering of EEG Waveforms[J].IEE Proceedings-Science,Measurement and Technology,2000,147(5):219-228.
[6]罗志增,李文国.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(4):823-827.
[7]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(6):1207-1213.
[8]Vigrio R N.Extraction of Ocular Artefacts from EEG Using Independent Component Analysis[J].Electroencephalography and Clinical Neurophysiology,1997,103(3):395-404.
[9]Kumar P S,Arumuganathan R,Sivakumar K,et al.Removal of Ocular Artifacts in the EEG through Wavelet Transform without Using an EOG Reference Channel[J].Int.J.Open Problems Compt Math,2008,1(3):188-200.
[10]刘然,徐苗,张艳珍,等.单通道脑电信号中眼电干扰的检测及去除方法[J],计算机应用,2017,37(S1):226-230,265.
[11]Bingham E,Hyvarinen A.A Fast Fixed-Point Algorithms for Independent Component Analysis of Complex Valued Signals[J].International Journal of Neural Systems,2000,10:1-8.
[12]席旭刚,朱海港,罗志增.基于EEMD和二代小波变换的表面肌电信号消噪方法[J].传感技术学报,2012,25(11):1488-1493.
[13]Torres M E,Colominas M A,Schlotthauer G,et al.A Complete Ensemble Empirical Mode Decomposition with Adaptive Noise[C]//2011 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP),Prague:IEEE,2011.4144-4147.
[14]Lee H M,Chen C M,Chen J M,et al.An Efficient Fuzzy Classifier with Feature Selection Based on Fuzzy Entropy[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B(Cybernetics),2001,31(3):426-432.
[15]Gómez-Herrero G,De Clercq W,Anwar H,et al.Automatic Removal of Ocular Artifacts in the EEG without an EOG Reference Channel[C]//Proceedings of the 7th Nordic Signal Processing Symposium(NORSIG 2006),Reykjavik:IEEE,2006.130-133.
[16]Chen X,Liu A,Peng H,et al.A Preliminary Study of Muscular Artifact Cancellation in Single-Channel EEG[J].Sensors,2014,14(10):18370-18389.
[17]Davies M E,James C J.Source Separation Using Single Channel ICA[J].Signal Processing,2007,87(8):1819-1832.
[2]马玉良,许明珍,佘青山,等.基于自适应阈值的脑电信号去噪方法[J].传感技术学报,2014,27(10):1368-1372.
[3]Tatum W O,Dworetzky B A,Schomer D L.Artifact and Recording Concepts in EEG[J].Journal of Clinical Neurophysiology,2011,28(3):252-263.
[4]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[J].电子与信息学报,2015,37,27(2):367-372.
[5]Vigon L,Saatchi M R,Mayhew J E W,et al.Quantitative Evaluation of Techniques for Ocular Artefact Filtering of EEG Waveforms[J].IEE Proceedings-Science,Measurement and Technology,2000,147(5):219-228.
[6]罗志增,李文国.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(4):823-827.
[7]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(6):1207-1213.
[8]Vigrio R N.Extraction of Ocular Artefacts from EEG Using Independent Component Analysis[J].Electroencephalography and Clinical Neurophysiology,1997,103(3):395-404.
[9]Kumar P S,Arumuganathan R,Sivakumar K,et al.Removal of Ocular Artifacts in the EEG through Wavelet Transform without Using an EOG Reference Channel[J].Int.J.Open Problems Compt Math,2008,1(3):188-200.
[10]刘然,徐苗,张艳珍,等.单通道脑电信号中眼电干扰的检测及去除方法[J],计算机应用,2017,37(S1):226-230,265.
[11]Bingham E,Hyvarinen A.A Fast Fixed-Point Algorithms for Independent Component Analysis of Complex Valued Signals[J].International Journal of Neural Systems,2000,10:1-8.
[12]席旭刚,朱海港,罗志增.基于EEMD和二代小波变换的表面肌电信号消噪方法[J].传感技术学报,2012,25(11):1488-1493.
[13]Torres M E,Colominas M A,Schlotthauer G,et al.A Complete Ensemble Empirical Mode Decomposition with Adaptive Noise[C]//2011 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP),Prague:IEEE,2011.4144-4147.
[14]Lee H M,Chen C M,Chen J M,et al.An Efficient Fuzzy Classifier with Feature Selection Based on Fuzzy Entropy[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B(Cybernetics),2001,31(3):426-432.
[15]Gómez-Herrero G,De Clercq W,Anwar H,et al.Automatic Removal of Ocular Artifacts in the EEG without an EOG Reference Channel[C]//Proceedings of the 7th Nordic Signal Processing Symposium(NORSIG 2006),Reykjavik:IEEE,2006.130-133.
[16]Chen X,Liu A,Peng H,et al.A Preliminary Study of Muscular Artifact Cancellation in Single-Channel EEG[J].Sensors,2014,14(10):18370-18389.
[17]Davies M E,James C J.Source Separation Using Single Channel ICA[J].Signal Processing,2007,87(8):1819-1832.