Please wait a minute...
J4  2013, Vol. 47 Issue (3): 415-421    DOI: 10.3785/j.issn.1008-973X.2013.03.004
    
Automatic ocular artifact removal based on blind source separation
JI Yu, SHEN Ji-zhong, SHI Jin-he
Institution of Electronic Circuit and Information System, Zhejiang University, Hangzhou 310027, China
Download:   PDF(0KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

Aiming at the problems such as the overestimation of ocular artifact, the need of human intervention, and the difficulty for online application in traditional blind source separation (BSS)-based methods for artifacts removal, a totally automatic method for removing ocular artifacts was proposed. BSS was used to separate electroencephalogram (EEG) signals to obtain the independent components. With the criterion of correlation coefficient, different time windows according to vertical electrooculogram (VEOG) and horizontal electrooculogram (HEOG)'s respective characteristics were used to find the best components combination, by which the time intervals that have blink or eye movement activities could be calibrated. Then, the calibrated time intervals were removed and the EEG signals were reconstructed. With experiments of P300 signals processing, this method was proved to be effective and practical in removing ocular artifact automatically and overcoming the drawbacks above. Compare with related literatures, the experiment results showed that the proposed method increased the average correlation coefficient between the reconstructed EEG signals and the original EEG signals from 0.8513 and 0.9006 to 0.9237 respectively, while the mean square error was decreased by 19.3% and 16.6%, contributing to online application.



Published: 01 March 2013
CLC:  TP 391  
Cite this article:

JI Yu, SHEN Ji-zhong, SHI Jin-he. Automatic ocular artifact removal based on blind source separation. J4, 2013, 47(3): 415-421.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2013.03.004     OR     http://www.zjujournals.com/eng/Y2013/V47/I3/415


一种基于盲源分离的眼电伪迹自动去除方法

为解决传统盲源分离算法(BSS)用于眼电伪迹去除大都存在伪迹过估计、需要人为辨别伪迹成分而不适合在线应用的不足,提出一种基于BSS算法的眼电伪迹自动去除方法. 利用BSS算法对脑电信号进行分离得到独立成分,以相关系数作为判据,针对垂直眼电(VEOG)和水平眼电(HEOG)的各自特点确定不同的时间窗,寻找最优成分组合标定眨眼或眼动活动发生的时域区间,将找到的存在伪迹的成分区间置零并重建脑电(EEG)信号. 通过真实P300脑电数据实验的结果表明:该方法能有效地自动去除眼电伪迹,且处理过程简单易行,克服了眼电伪迹过估计等问题. 算法重建EEG信号与原始脑电(EEG)信号的平均相关系数分别从0.8513和0.9006提高到0.9237,而均方误差分别减少了19.3%和16.6%,适合在线应用.

[1] WANG Z, BOVIK A C. Modern image quality assessment [M]. San Rafael, CA: Morgan & Claypool, 2006.
[2] 王翔. 数字图像缩放及图像质量评价关键技术研究 [D]. 杭州: 浙江大学, 2012: 7592.
WANG Xiang. Research on image scaling and image quality assessment [D]. Hangzhou: Zhejiang University, 2012: 75-92.
[3] VQEG. Final report from the video quality experts group on the validation of objective models of video quality assessment [EB/OL]. [2000-03-05]. http :// www. vqeg. org/.
[4] MIYAHARA M, KOTANI K, ALGAZI V R. Objective picture quality scale (PQS) for image coding [J]. IEEE Transactions on Communications, 1998, 46(9): 1215-1225.
[5] WANG Z, BOVIK A C. A universal image quality index [J]. IEEE, Signal Processing Letters, 2002, 9(3): 81-84.
[6] WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity [J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.
[7] WANG Z, SIMONCELLI E P, BOVIK A C. Multi-scale structural similarity for image quality assessment [C]∥ Proceedings of the 37th IEEE Asilomar Conference on Signals, Systems and Computers. California: IEEE, 2003: 1398-1402.
[8] RAO D V, REDDY L P. Contrast weighted perceptual structural similarity index for image quality assessment [J]. Journal of Electronic Imaging, 2010, 19(1): 011003-1-9.
[9] LAM E P, LOO K C. An image similarity measure using homogeneity regions and structure[C]∥ Proceedings of SPIE, Image Quality and System Performance V. 2008, 6808: 680-711.
[10] SHEIKH H R, BOVIK A C, VECIANA G DE. An information fidelity criterion for image quality assessment using natural scene statistics [J]. IEEE Transactions on Image Processing, 2005, 14(12): 2117-2128.
[11] TAO D, LI X, LU W, et al. Reduced-reference IQA in contourlet domain[J]. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2009, 39(6): 1623-1627.
[12] LI Q, WANG Z. Reduced-reference image quality assessment using divisive normalization-based image representation [J]. IEEE Journal of Selected Topics in Signal Processing, 2009, 3(2): 202-211.
[13] FERZLI R, KARAM L J. A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB) [J]. IEEE Transactions on Image Processing, 2009, 18(4): 717-728.
[14] SHEIKH H R, BOVIK A C, CORMACK L. No-reference quality assessment using natural scene statistics: JPEG2000[J]. IEEE Transactions on Image Processing, 2005, 14(11): 1918-1927.
[15] ECKERT M P, BRADLEY A P. Perceptual quality metrics applied to still image compression [J]. Signal Processing Special Issue on Image and Video Quality Metric, 1998, 70: 177-200.
[16] DAUGMAN J G. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters [J]. Journal of the Optical Society of America A Optics and Image Science, 1985(2): 1160-1169.
[17] DE VALOIS R L, ALBRECHT D G, THORELL L G. The orientation and direction selectivity of cells in macaque visual cortex [J]. Vision Research, 1982, 22: 531-544.
[18] WANG Z, SIMONCELLI E P. Translation insensitive image similarity in complex wavelet domain [C]∥ IEEE International Conference on Acoustics, Speech & Signal Processing. Philadelphia: IEEE, 2005:573-576.
[19] BROOKS A C, ZHAO X N, PAPPAS T N. Structural similarity quality metrics in a coding context: exploring the space of realistic distortions[J]. IEEE Transactions on Image Processing, 2008, 17(8): 1261-1273.
[20] SHEIKH H R, WANG Z, CORMACK L, et al. LIVE image quality assessment database release 2[EB/OL]. [2011-11-14]. http:∥live.ece.utexas.edu/research/quality.
[21] SARNOFF CORPORATION. JNDmetrix Technology [EB/OL]. [2011-12-05]. http :∥www. sarnoff. com/ products services/ video vision/ jndmetrix/ downloads. asp.

[1] ZHAO Jian-jun, WANG Yi, YANG Li-bin. Threat assessment method based on time series forecast[J]. J4, 2014, 48(3): 398-403.
[2] CUI Guang-mang, ZHAO Ju-feng,FENG Hua-jun, XU Zhi-hai,LI Qi, CHEN Yue-ting. Construction of fast simulation model for degraded image by inhomogeneous medium[J]. J4, 2014, 48(2): 303-311.
[3] ZHANG Tian-yu, FENG Hua-jun, XU Zhi-hai, LI Qi, CHEN Yue-ting. Sharpness metric based on histogram of strong edge width[J]. J4, 2014, 48(2): 312-320.
[4] LIU Zhong, CHEN Wei-hai, WU Xing-ming, ZOU Yu-hua, WANG Jian-hua. Salient region detection based on stereo vision[J]. J4, 2014, 48(2): 354-359.
[5] WANG Xiang-bing,TONG Shui-guang,ZHONG Wei,ZHANG Jian. Study on  scheme design technique for hydraulic excavator's structure performance based on extension reuse[J]. J4, 2013, 47(11): 1992-2002.
[6] WANG Jin, LU Guo-dong, ZHANG Yun-long. Quantification-I theory based IGA and its application[J]. J4, 2013, 47(10): 1697-1704.
[7] LIU Yu, WANG Guo-jin. Designing  developable surface pencil through  given curve as its common asymptotic curve[J]. J4, 2013, 47(7): 1246-1252.
[8] HU Gen-sheng, BAO Wen-xia, LIANG Dong, ZHANG Wei. Fusion of panchromatic image and multi-spectral image based on
SVR and Bayesian method 
[J]. J4, 2013, 47(7): 1258-1266.
[9] WU Jin-liang, HUANG Hai-bin, LIU Li-gang. Texture details preserving seamless image composition[J]. J4, 2013, 47(6): 951-956.
[10] CHEN Xiao-hong,WANG Wei-dong. A HDTV video de-noising algorithm based on spatial-temporal filtering[J]. J4, 2013, 47(5): 853-859.
[11] ZHU Fan , LI Yue, JIANG Kai, YE Shu-ming, ZHENG Xiao-xiang. Decoding of rat’s primary motor cortex by partial least square[J]. J4, 2013, 47(5): 901-905.
[12] WU Ning, CHEN Qiu-xiao, ZHOU Ling, WAN Li. Multi-level method of optimizing vector graphs converted from remote sensing images[J]. J4, 2013, 47(4): 581-587.
[13] WANG Xiang, DING Yong. Full reference image quality assessment based on Gabor filter[J]. J4, 2013, 47(3): 422-430.
[14] TONG Shui-guang, WANG Xiang-bing, ZHONG Wei, ZHANG Jian. Dynamic optimization design for rigid landing leg of crane
based on BP-HGA
[J]. J4, 2013, 47(1): 122-130.
[15] LIU Fang, SUN Yun, YANG Geng, LIN Hai. Visualization of social network based on particle swarm optimization[J]. J4, 2013, 47(1): 37-43.