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基于過完備字典稀疏表示的多通道腦電信號壓縮感知聯(lián)合重構(gòu)

吳建寧,  徐海東,  王玨

吳建寧, 徐海東, 王玨. 基于過完備字典稀疏表示的多通道腦電信號壓縮感知聯(lián)合重構(gòu)[J]. 電子與信息學(xué)報, 2016, 38(7): 1666-1673. doi: 10.11999/JEIT151079
引用本文: 吳建寧, 徐海東, 王玨. 基于過完備字典稀疏表示的多通道腦電信號壓縮感知聯(lián)合重構(gòu)[J]. 電子與信息學(xué)報, 2016, 38(7): 1666-1673. doi: 10.11999/JEIT151079
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]. Journal of Electronics & Information Technology, 2016, 38(7): 1666-1673. doi: 10.11999/JEIT151079
Citation: 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]. Journal of Electronics & Information Technology, 2016, 38(7): 1666-1673. doi: 10.11999/JEIT151079

基于過完備字典稀疏表示的多通道腦電信號壓縮感知聯(lián)合重構(gòu)

doi: 10.11999/JEIT151079
  • 1.

    (福建師范大學(xué)數(shù)學(xué)與計算機科學(xué)學(xué)院 福州 350007) ②(西安交通大學(xué)生物醫(yī)學(xué)信息工程教育部重點實驗室 西安 710049)

基金項目: 

國家科技支撐項目(2012BAI33B01)，福建省自然科學(xué)基金項目(2013J01220)，福建省高等學(xué)校教學(xué)改革研究項目(JAS14674)，福建師范大學(xué)創(chuàng)新創(chuàng)業(yè)教育改革研究項目(D201503005)

A New Joint Reconstruction Algorithm of Compressed Sensing for Multichannel EEG Signals Based on Over-complete Dictionary Approach

  • 1.

    (School of Mathematics and Computer Science, Fujian Normal University, Fuzhou 350007, China)

  • 2.

    (Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi&rsquo

Funds: 

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)

  • 摘要: 該文基于多通道腦電信號時空特性構(gòu)建非正交變換過完備字典，準確稀疏表示蘊含時空相關(guān)性信息的多通道腦電信號，提高基于時空稀疏貝葉斯學(xué)習模型的多通道腦電信號壓縮感知聯(lián)合重構(gòu)算法性能。實驗選用eegmmidb腦電數(shù)據(jù)庫的多通道腦電信號驗證所提算法有效性。結(jié)果表明，基于過完備字典稀疏表示的多通道腦電信號，能夠為多通道腦電信號壓縮感知重構(gòu)算法提供更多的時空相關(guān)性信息，比傳統(tǒng)多通道腦電信號壓縮感知重構(gòu)算法所得的信噪比值提高近12 dB，重構(gòu)時間減少0.75 s，顯著提高多通道腦電信號聯(lián)合重構(gòu)性能。
    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.
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出版歷程
  • 收稿日期:  2015-09-21
  • 修回日期:  2016-04-29
  • 刊出日期:  2016-07-19

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