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

WU Jianning; XU Haidong; WANG Jue

doi:10.11999/JEIT151079

Volume 38 Issue 7

Jul. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(7): 1666-1673

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

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

doi: 10.11999/JEIT151079

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)

Received Date: 2015-09-21
Rev Recd Date: 2016-04-29
Publish Date: 2016-07-19

Abstract

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

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References(27)

References

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