Compressive Sensing of Multichannel EEG Signals via l q Norm and Schatten- p Norm Regularization
Joint Authors
Chen, Changwei
Su, Shoubao
Chang, Zinan
Zhu, Jun
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-11-14
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
In Wireless Body Area Networks (WBAN) the energy consumption is dominated by sensing and communication.
Recently, a simultaneous cosparsity and low-rank (SCLR) optimization model has shown the state-of-the-art performance in compressive sensing (CS) recovery of multichannel EEG signals.
How to solve the resulting regularization problem, involving l 0 norm and rank function which is known as an NP-hard problem, is critical to the recovery results.
SCLR takes use of l 1 norm and nuclear norm as a convex surrogate function for l 0 norm and rank function.
However, l 1 norm and nuclear norm cannot well approximate the l 0 norm and rank because there exist irreparable gaps between them.
In this paper, an optimization model with l q norm and schatten- p norm is proposed to enforce cosparsity and low-rank property in the reconstructed multichannel EEG signals.
An efficient iterative scheme is used to solve the resulting nonconvex optimization problem.
Experimental results have demonstrated that the proposed algorithm can significantly outperform existing state-of-the-art CS methods for compressive sensing of multichannel EEG channels.
American Psychological Association (APA)
Zhu, Jun& Chen, Changwei& Su, Shoubao& Chang, Zinan. 2016. Compressive Sensing of Multichannel EEG Signals via l q Norm and Schatten- p Norm Regularization. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1111859
Modern Language Association (MLA)
Zhu, Jun…[et al.]. Compressive Sensing of Multichannel EEG Signals via l q Norm and Schatten- p Norm Regularization. Mathematical Problems in Engineering No. 2016 (2016), pp.1-7.
https://search.emarefa.net/detail/BIM-1111859
American Medical Association (AMA)
Zhu, Jun& Chen, Changwei& Su, Shoubao& Chang, Zinan. Compressive Sensing of Multichannel EEG Signals via l q Norm and Schatten- p Norm Regularization. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1111859
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1111859