便携式BCI设备快速自动去眼电伪迹算法的研究
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摘要
针对多通道的去眼电伪迹研究目前已较为成熟,但是在便携式单通道脑电信号领域,尚未有一种十分有效快速去除眼电伪迹的方法。经验小波变换EWT是一种新型的自适应信号处理算法,相较于经验模态分解EMD算法存在模态混跌问题和集合经验模态分解EEMD算法实时性不足的缺点,EWT将小波变换和EMD相结合克服了前者的缺点。基于此提出将EWT、典型相关分析CCA以及瑞利熵RE相结合的自动去眼电伪迹算法。试验表明,该方法可有效去除单通道脑电中的眼电伪迹,且快速自动,能满足便携式单通道脑机接口BCI的需求。
The research on multichannel removal electrooculogram(EOG) artifacts is relatively mature,but in the field of portable single-channel electroencephalogram(EEG) signals,there is no effective method for removing EOG artifacts very quickly. Empirical wavelet transform(EWT) is a new type of adaptive signal processing algorithm. The empirical mode decomposition(EMD) algorithm has a modal mixed-fall problem and the ensemble EMD(EEMD) algorithm has the disadvantage of lack of real-time performance. EWT combines wavelet transform and EMD to overcome the shortcomings of the former. Therefore,an automatic eccentricity artifact algorithm combining EWT,canonical correlation analysis(CCA) and Rayleigh entropy(RE) is proposed in this paper. Experiments show that this method can effectively remove the EOG artifacts in single-channel EEG,and has the advantages of fast and automatic,which can meet the needs of portable single-channel brain computer interface(BCI).
The research on multichannel removal electrooculogram(EOG) artifacts is relatively mature,but in the field of portable single-channel electroencephalogram(EEG) signals,there is no effective method for removing EOG artifacts very quickly. Empirical wavelet transform(EWT) is a new type of adaptive signal processing algorithm. The empirical mode decomposition(EMD) algorithm has a modal mixed-fall problem and the ensemble EMD(EEMD) algorithm has the disadvantage of lack of real-time performance. EWT combines wavelet transform and EMD to overcome the shortcomings of the former. Therefore,an automatic eccentricity artifact algorithm combining EWT,canonical correlation analysis(CCA) and Rayleigh entropy(RE) is proposed in this paper. Experiments show that this method can effectively remove the EOG artifacts in single-channel EEG,and has the advantages of fast and automatic,which can meet the needs of portable single-channel brain computer interface(BCI).
引文
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[12] Khatun S,Mahajan R,Morshed B I. Comparative study of waveletbased unsupervised ocular artifact removal techniques for single-channel EEG data[J].IEEE Journal of Translational Engineering in Health&Medicine,2016,2016(4):1-8.
[2] Maddirala A K,Shaik R A. Removal of EOG artifacts from single channel EEG signals us ing combined singular spectrum analysis and adaptive noise canceler[J].IEEE Sensors Journal,2016,16(23):8279-8287.
[3] Mammone N,Foresta F L,Morabito F C. Automatic artifact rejection from multichannel scalp EEG by wavelet ICA[J].IEEE Sensors Journal,2012,12(3):533-542.
[4] Mijovi'c B,De V M,Gligorijevi'c I,et al. Source separation from single-channel recordings by combining empirical-mode decomposition and independent component analysis[J].IEEE Transactions on Biomedical Engineering,2010,57(9):2188-2196.
[5]刘志勇,孙金玮,卜宪庚.单通道脑电信号眼电伪迹去除算法研究[J].自动化学报,2017,43(10):1726-1735.
[6] Gilles J. Empirical wavelet transform[J].IEEE Transactions on Signal Processing,2013,61(16):3999-4010.
[7]张莉,何传红,何为.典型相关分析去除脑电信号中眼电伪迹的研究[J].计算机工程与应用,2009,45(31):218-220.
[8] Maddirala A K,Shaik R A. Motion artifact removal from single channel electroencephalogram signals using singular spectrum analysis[J].Biomedical Signal Processing&Control,2016,2016(30):79-85.
[9] De C W,Vergult A,Vanrumste B,et al. Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram[J].IEEE Transactions on Biomedical Engineering,2006,53(12):2583-2587.
[10] Gao J,Hu J,Tung W W. Entropy measures for biological signal analyses[J].Nonlinear Dynamics,2012,68(68):431-444.
[11] Feltane A,Bartels G F B,Gaitanis J,et al. Human seizure detection using quadratic Rényi entropy[C]//International Ieee/embs Conference on Neural Engineering. IEEE,2013:815-818.
[12] Khatun S,Mahajan R,Morshed B I. Comparative study of waveletbased unsupervised ocular artifact removal techniques for single-channel EEG data[J].IEEE Journal of Translational Engineering in Health&Medicine,2016,2016(4):1-8.