Compressed Sensing of Multichannel EEG Signals: The Simultaneous Cosparsity and Low-Rank Optimization

被引：32

作者：

Liu, Yipeng ^{[1
]}

De Vos, Maarten ^{[2
]}

Van Huffel, Sabine ^{[1
]}

机构：

[1] Univ Leuven, ESAT STADIUS Div, IMinds Med IT Dept, Dept Elect Engn, B-3001 Leuven, Belgium

[2] Univ Oxford, Inst Biomed Engn, Dept Engn, Oxford, England

来源：

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | 2015年 / 62卷 / 08期

关键词：

Alternating direction method of multipliers (ADMM); compressed sensing (CS); cosparse signal recovery; low-rank matrix recovery; multichannel electroencephalogram (EEG); MATRIX;

D O I：

10.1109/TBME.2015.2411672

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Goal: This paper deals with the problems that some EEG signals have no good sparse representation and single-channel processing is not computationally efficient in compressed sensing of multichannel EEG signals. Methods: An optimization model with L0 norm and Schatten-0 norm is proposed to enforce cosparsity and low-rank structures in the reconstructed multichannel EEG signals. Both convex relaxation and global consensus optimization with alternating direction method of multipliers are used to compute the optimization model. Results: The performance of multichannel EEG signal reconstruction is improved in term of both accuracy and computational complexity. Conclusion: The proposed method is a better candidate than previous sparse signal recovery methods for compressed sensing of EEG signals. Significance: The proposed method enables successful compressed sensing of EEG signals even when the signals have no good sparse representation. Using compressed sensing would much reduce the power consumption of wireless EEG system.

引用

页码：2055 / 2061

页数：7

共 50 条

[41] Improving synthesis and analysis prior blind compressed sensing with low-rank constraints for dynamic MRI reconstruction
Majumdar, Angshul
MAGNETIC RESONANCE IMAGING, 2015, 33 (01) : 174 - 179
[42] Compressed FTIR spectroscopy using low-rank matrix reconstruction
Marschall, Manuel
Hornemann, Andrea
Wuebbeler, Gerd
Hoehl, Arne
Ruehl, Eckart
Kaestner, Bernd
Elster, Clemens
OPTICS EXPRESS, 2020, 28 (26) : 38762 - 38772
[43] Cardiac diffusion tensor imaging based on compressed sensing using joint sparsity and low-rank approximation
Huang, Jianping
Wang, Lihui
Chu, Chunyu
Zhang, Yanli
Liu, Wanyu
Zhu, Yuemin
TECHNOLOGY AND HEALTH CARE, 2016, 24 : S593 - S599
[44] Compressed Randomized UTV Decompositions for Low-Rank Matrix Approximations
Kaloorazi, Maboud F.
de Lamare, Rodrigo C.
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2018, 12 (06) : 1155 - 1169
[45] EXACT RECOVERY OF LOW-RANK PLUS COMPRESSED SPARSE MATRICES
Mardani, Morteza
Mateos, Gonzalo
Giannakis, Georgios B.
2012 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP), 2012, : 49 - 52
[46] A Riemannian rank-adaptive method for low-rank optimization
Zhou, Guifang
Huang, Wen
Gallivan, Kyle A.
Van Dooren, Paul
Absil, Pierre-Antoine
NEUROCOMPUTING, 2016, 192 : 72 - 80
[47] Adaptive Detection of Structured Signals in Low-Rank Interference
Schniter, Philip
Byrne, Evan
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2019, 67 (13) : 3439 - 3454
[48] Adaptive Detection of Structured Signals in Low-Rank Interference
Schniter, Philip
Byrne, Evan
2018 CONFERENCE RECORD OF 52ND ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS, 2018, : 1810 - 1814
[49] On optimal low-rank approximation of multidimensional discrete signals
Lu, WS
Pei, SC
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-ANALOG AND DIGITAL SIGNAL PROCESSING, 1998, 45 (03): : 417 - 422
[50] FPGA-based real-time compressed sensing of multichannel EEG signals for wireless body area networks
Liu, Dan
Wang, Qisong
Zhang, Yan
Liu, Xin
Lu, Jingyang
Sun, Jinwei
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2019, 49 : 221 - 230

← 1 2 3 4 5 →