A New Joint Reconstruction Algorithm of Compressed Sensing for Multichannel EEG Signals Based on Over-complete Dictionary Approach
WU Jianning① XU Haidong① WANG Jue②
①(School of Mathematics and Computer Science, Fujian Normal University, Fuzhou 350007, China) ②(Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi’an Jiaotong University, Xi’an 710049, China)
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.
吴建宁,徐海东,王珏. 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[J]. 电子与信息学报, 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. JEIT, 2016, 38(7): 1666-1673.
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