基于过完备字典稀疏表示的多通道脑电信号压缩感知联合重构

doi:10.11999/JEIT151079

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Abstract

In this paper, the over-complete dictionary with nonorthogonal factor is firstly gained from Electro Encephalo Graph (EEG) signal with spatio-temporal characteristics, and then it is used to sparsely represent multichannel EEG signal for containing the information of spatio-temporal correlation. This contributes to enhance the performance of the joint reconstruction of multi-channel EEG signal using the Spatio-Temporal Sparse Bayesian Learning (STSBL) algorithm. The multi-channel EEG signal from the open eegmmidb database are selected to evaluate the effectiveness of the proposed algorithm. The experimental results show that the designed over-complete dictionary can provide more valuable information about the spatio-temporal characteristics in multichannel EEG signal for STSBL algorithm. When compared to the existing conventional compressed sensing technique for reconstruction multi-channel EEG signal, the signal-noise ratio of the proposed method increases by 12 dB and the reconstruction time decreases by 0.75 s, which significantly improve the performance of joint reconstruction of multichannel EEG signal.

Key words： Sparse representation of EEG signal Over-complete dictionary Joint reconstruction Spatio-temporal sparse Bayesian learning Compressed Sensing (CS)

Received: 21 September 2015 Published: 31 May 2016

PACS:	R741.044
	TP393

Fund:

The National Science and Technology Supporting Project (2012BAI33B01), The Natural Science Foundation of Fujian Province (2013J01220), The Teaching Reform Project of University of Fujian Province (JAS14674), The Project of Education of Entrepreneurship and Innovation of Fujian Normal University (D201503005)

Corresponding Authors: WU Jianning E-mail: jianningwu@fjnu.edu.cn

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	WU Jianning
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	WANG Jue

Cite this article:

WU Jianning,XU Haidong,WANG Jue. A New Joint Reconstruction Algorithm of Compressed Sensing for Multichannel EEG Signals Based on Over-complete Dictionary Approach[J]. JEIT, 2016, 38(7): 1666-1673.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT151079 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I7/1666

[1]	WESTOVER M B, SHAFI M, BIANCHI M T, et al. The probability of seizures during EEG monitoring in critically ill adults[J]. Clinical Neurophysiology Official Journal of the International Federation of Clinical Neurophysiology, 2015, 126(3): 463-471. doi: 10.1016/J.clinph.2014.05.037.
[2]	KIM Y and LEE S K. Energy-efficient wireless hospital sensor networking for remote patient monitoring[J]. Information Sciences, 2014, 282: 332-349. doi: 10.1016/ j.ins.2014.05.056.
[3]	DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. doi: 10.1109/ TIT.2006.871582.
[4]	FAUVEL S and WARD R K. An energy efficient compressed sensing framework for the compression of Electroencephalogram signals[J]. Sensors, 2014, 14(1): 1474-1496. doi: 10.3390/s140101474.
[5]	LIU B, ZHANG Z, XU G, et al. Energy efficient telemonitoring of physiological signals via compressed sensing: a fast algorithm and power consumption evaluation[J]. IEEE Transactions on Biomedical Signal Processing and Control, 2013, 11(1): 80-88. doi: 10.1016/j.bspc.2014.02.010.
[6]	CLIFTON L, CLIFTON D A, PIMENTEL M A, et al. Predictive monitoring of mobile patients by combining clinical observations with data from wearable sensor[J]. IEEE Journal of Biomedical and Health Informatics, 2014, 18(3): 722-730. doi: 10.1109/JBHI.2013.2293059.
[7]	ZHANG Z, JUNG T P, MAKEIG S, et al. Compressed sensing of EEG for wireless telemonitoring with low energy consumption and inexpensive hardware[J]. IEEE Transactions on Biomedical Engineering, 2013, 60(1): 221-224. doi: 10.1109/TBME.2012.2217959.
[8]	DAI Y, WANG X, LI X, et al. Sparse EEG compressive sensing for web-enabled person identification[J]. Measurement, 2015, 74: 11-20. doi: 10.1016/j.measurement. 2015.07.008.
[9]	BARANIUK R G, CEVHER V, DUARTE M F, et al. Model-based compressive sensing[J]. IEEE Transactions on Information Theory, 2010, 56(4): 1982-2001. doi: 10.1109/ TIT.2010.2040894.
[10]	TROPP J A and GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666. doi: 10.1109/TIT.2007.909108.
[11]	DAI W and MILENKOVIC O. Subspace pursuit for compressive sensing signal reconstruction[J]. IEEE Transactions on Information Theory, 2009, 55(5): 2230-2249. doi: 10.1109/TIT.2009.2016006.
[12]	MOHIMANI H, BABAIE-ZADEH M, and JUTTEN C. A fast approach for overcomplete sparse decomposition based on smoothed l0 norm[J]. IEEE Transactions on Signal Processing. 2009, 57(1): 289-301. doi: 10.1109/TSP.2008. 2007606.
[13]	ZHANG Z, JUNG T P, MAKEIG S, et al. Compressed sensing for energy-efficient wireless telemonitoring of noninvasive fetal ECG via block sparse Bayesian learning[J]. IEEE Transactions on Biomedical Engineering, 2013, 60(2): 300-309. doi: 10.1109/TBME.2012.2226175.
[14]	孙洪, 张智林, 余磊. 从稀疏到结构化稀疏: 贝叶斯方法[J]. 信号处理, 2012, 28(6): 759-773. doi: 10.16798/j.issn.1003- 0530.2016.02.005.
	SUN Hong, ZHANG Zhilin, and YU Lei. From sparsity to structured sparsity: Bayesian perspective[J]. Signal Processing, 2012, 28(6): 759-773. doi: 10.16798/j.issn.1003- 0530.2016.02.005.
[15]	ZHANG Z and RAO B D. Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation[J]. IEEE Transactions on Signal Processing, 2013, 61(8): 2009-2015. doi: 10.1109/TSP.2013.2241055.
[16]	COTTER S F, RAO B D, ENGAN K, et al. Sparse solutions to linear inverse problems with multiple measurement vectors [J]. IEEE Transactions on Signal Processing, 2005, 53(7): 2477-2488. doi: 10.1109/TSP.2005.849172.
[17]	ELDAR Y C and RAUHUT H. Average case analysis of multichannel sparse recovery using convex relaxation[J]. IEEE Transactions on Information Theory, 2010, 56(1): 505-519. doi: 10.1109/TIT.2009.2034789.
[18]	ZHANG Z and RAO B D. Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(5): 912-926. doi: 10.1109/JSTSP.2011. 2159773.
[19]	ZHANG Z, JUNG T P, and MAKEIG S. Spatiotemporal sparse Bayesian learning with applications to compressed sensing of multichannel physiological signals[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014, 22(6): 1186-1197. doi: 10.1109/TNSRE. 2014.2319334.
[20]	LEE S J, LUAN J, and CHOU P H. ECG signal reconstruction from undersampled measurement using a trained overcomplete dictionary[J]. Contemporary Engineering Science, 2014, 7(29): 1625-1632. doi: http://dx. doi.org/10.12988/ces.2014.41121.
[21]	彭向东, 张华, 刘继忠. 基于过完备字典的体域网压缩感知心电重构[J]. 自动化学报, 2014, 40(7): 1421-1432. doi: 10.3724/ SP.J.1004.2014.01421.
	PENG Xiangdong, ZHANG Hua, and LIU Jizhong. ECG reconstruction of body sensor network using compressed sensing based on overcomplete dictionary[J]. Acta Automatica Sinica, 2014, 40(7): 1421-1432. doi: 10.3724/SP.J. 1004.2014.01421.
[22]	孙林慧, 杨震, 季云云, 等. 基于过完备线性预测字典的压缩感知语音重构[J]. 仪器仪表学报, 2012, 33(4): 743-749. doi: 10.3969/j.issn.0254-3087.2012.04.004.
	SUN Linhui, YANG Zhen, JI Yunyun, et al. Reconstruction of compressed speech sensing based on overcomplete linear prediction dictionary[J]. Chinese Journal of Scientific Instrument, 2012, 33(4): 743-749. doi: 10.3969/j.issn.0254- 3087.2012.04.004.
[23]	DONEVA M, BORNERT P, EGGERS H, et al. Compressed sensing reconstruction for magnetic resonance parameter mapping[J]. Magnetic Resonance in Medicine, 2010, 64(4): 1114-1120. doi: 10.1002/mrm.22483.
[24]	AHARON M, ELAD M, and BRUCKSTEIN A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322. doi: 10.1109/TSP.2006.881199.
[25]	SCHALLK G, MCFARLAND D J, HINTERBERGER T, et al. BCI2000: a general-purpose Brain-Computer Interface (BCI) system[J]. IEEE Transactions on Biomedical Engineering, 2004, 51(6): 1034-1043. doi: 10.1109/TBME. 2004.827072.