Evoked Potential Blind Extraction Based on Fractional Lower Order Spatial Time-Frequency Matrix_Journal of Biomedical Engineering

Authors：

 LONGJunbo ¹ , WANGHaibin ² , ZHADaifeng ³

1. College of Electronic and Engineering, Jiujiang University, Jiujiang 332005, China;
2. College of Information Science and Technology, Jiujiang University, Jiujiang 332005, China;
3. College of Science, Jiujiang University, Jiujiang 332005, China;

Corresponding author：

LONGJunbo, Email: 18488870@qq.com

Keywords：

alpha (α) stable distribution; biomedical signal; evoked potential; blind source separation; fractional lower order

DOI：

10.7507/1001-5515.20150049

Video：

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Abstract Full text Figures/Tables Video References Cited by

The impulsive electroencephalograph (EEG) noises in evoked potential (EP) signals is very strong, usually with a heavy tail and infinite variance characteristics like the acceleration noise impact, hypoxia and etc., as shown in other special tests. The noises can be described by α stable distribution model. In this paper, Wigner-Ville distribution (WVD) and pseudo Wigner-Ville distribution (PWVD) time-frequency distribution based on the fractional lower order moment are presented to be improved. We got fractional lower order WVD (FLO-WVD) and fractional lower order PWVD (FLO-PWVD) time-frequency distribution which could be suitable for α stable distribution process. We also proposed the fractional lower order spatial time-frequency distribution matrix (FLO-STFM) concept. Therefore, combining with time-frequency underdetermined blind source separation (TF-UBSS), we proposed a new fractional lower order spatial time-frequency underdetermined blind source separation (FLO-TF-UBSS) which can work in α stable distribution environment. We used the FLO-TF-UBSS algorithm to extract EPs. Simulations showed that the proposed method could effectively extract EPs in EEG noises, and the separated EPs and EEG signals based on FLO-TF-UBSS were almost the same as the original signal, but blind separation based on TF-UBSS had certain deviation. The correlation coefficient of the FLO-TF-UBSS algorithm was higher than the TF-UBSS algorithm when generalized signal-to-noise ratio (GSNR) changed from 10 dB to 30 dB and α varied from 1.06 to 1.94, and was approximately equal to 1. Hence, the proposed FLO-TF-UBSS method might be better than the TF-UBSS algorithm based on second order for extracting EP signal under an EEG noise environment.

Citation： LONGJunbo, WANGHaibin, ZHADaifeng. Evoked Potential Blind Extraction Based on Fractional Lower Order Spatial Time-Frequency Matrix. Journal of Biomedical Engineering, 2015, 32(2): 269-274. doi: 10.7507/1001-5515.20150049 Copy

1.	LIU Hongtao, CHANG C Q, LUK K D K, et al. Comparison of blind source separation methods in fast somatosensory-evoked potential detection[J]. J Clin Neurophysiol, 2011, 28(2):170-177.
2.	RAMIREZ-CORTES J M, ALARCON-AQUINO V, ROSAS-CHOLULA G, et al. Anfis-based P300 rhythm detection using wavelet feature extraction on blind source separated EEG signals[M]//AO S-L, AMOUZEGAR M, RIEGER B B. Intelligent Automation and Systems Engineering. New York:Springer New York, 2011, 103:353-365.
3.	KLADOS M A, PAPADELIS C, BRAUN C, et al. REG-ICA:A hybrid methodology combining Blind Source Separation and regression techniques for the rejection of ocular artifacts[J]. Biomed Signal Process Control, 2011, 6(3):291-300.
4.	AHMADIAN P, SANEI S, ASCARI L, et al. Constrained blind source extraction of readiness potentials from EEG[J]. IEEE Trans Neural Syst Rehabil Eng, 2013, 21(4):567-575.
5.	SAHMOUDI M, ABED-MERAIM K, BENIDIR M. Blind separation of impulsive alpha-stable sources using minimum dispersion criterion[J]. IEEE Signal Process Lett, 2005, 12(4):281-284.
6.	查代奉, 杨耀防, 车向新, 等.低阶非高斯噪声下基于BOREL谱测度的诱发电位少次提取方法[J].中国生物医学工程学报, 2009, 28(2):177-182.
7.	林政剑, 查代奉, 盛健.基于共变的非高斯噪声中诱发电位的盲分离方法[J].生物医学工程学杂志, 2010, 27(4):727-730.
8.	郭玙, 邱天爽, 李小兵, 等.Alpha稳定分布噪声下单路EP信号的动态提取方法[J].中国生物医学工程学报, 2008, 27(5):684-688.
9.	AISSA-EL-BEY A, NGUYEN L-T, ABED-MERAIM K, et al. Underdetermined blind separation of nondisjoint sources in the time-frequency domain[J]. IEEE Trans Sig Process, 2007, 55(3):897-907.
10.	陆凤波, 黄知涛, 彭耿, 等.基于时频分布的欠定混叠盲分离[J].电子学报, 2011, 39(9):2067-2072.

1. LIU Hongtao, CHANG C Q, LUK K D K, et al. Comparison of blind source separation methods in fast somatosensory-evoked potential detection[J]. J Clin Neurophysiol, 2011, 28(2):170-177.
2. RAMIREZ-CORTES J M, ALARCON-AQUINO V, ROSAS-CHOLULA G, et al. Anfis-based P300 rhythm detection using wavelet feature extraction on blind source separated EEG signals[M]//AO S-L, AMOUZEGAR M, RIEGER B B. Intelligent Automation and Systems Engineering. New York:Springer New York, 2011, 103:353-365.
3. KLADOS M A, PAPADELIS C, BRAUN C, et al. REG-ICA:A hybrid methodology combining Blind Source Separation and regression techniques for the rejection of ocular artifacts[J]. Biomed Signal Process Control, 2011, 6(3):291-300.
4. AHMADIAN P, SANEI S, ASCARI L, et al. Constrained blind source extraction of readiness potentials from EEG[J]. IEEE Trans Neural Syst Rehabil Eng, 2013, 21(4):567-575.
5. SAHMOUDI M, ABED-MERAIM K, BENIDIR M. Blind separation of impulsive alpha-stable sources using minimum dispersion criterion[J]. IEEE Signal Process Lett, 2005, 12(4):281-284.
6. 查代奉, 杨耀防, 车向新, 等.低阶非高斯噪声下基于BOREL谱测度的诱发电位少次提取方法[J].中国生物医学工程学报, 2009, 28(2):177-182.
7. 林政剑, 查代奉, 盛健.基于共变的非高斯噪声中诱发电位的盲分离方法[J].生物医学工程学杂志, 2010, 27(4):727-730.
8. 郭玙, 邱天爽, 李小兵, 等.Alpha稳定分布噪声下单路EP信号的动态提取方法[J].中国生物医学工程学报, 2008, 27(5):684-688.
9. AISSA-EL-BEY A, NGUYEN L-T, ABED-MERAIM K, et al. Underdetermined blind separation of nondisjoint sources in the time-frequency domain[J]. IEEE Trans Sig Process, 2007, 55(3):897-907.
10. 陆凤波, 黄知涛, 彭耿, 等.基于时频分布的欠定混叠盲分离[J].电子学报, 2011, 39(9):2067-2072.

Journal of Biomedical Engineering

Evoked Potential Blind Extraction Based on Fractional Lower Order Spatial Time-Frequency Matrix

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