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Mathematical Problems in Engineering
Volume 2016, Article ID 2189563, 7 pages
Research Article

Compressive Sensing of Multichannel EEG Signals via Norm and Schatten- Norm Regularization

1School of Computer Engineering, Jinling Institute of Technology, Nanjing 211169, China
2College of Computer and Information Engineering, Nanjing Xiaozhuang University, Nanjing 210017, China
3Jinling Institute of Technology, Nanjing, China

Received 11 June 2016; Revised 25 September 2016; Accepted 19 October 2016

Academic Editor: Raffaele Solimene

Copyright © 2016 Jun Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 norm and rank function which is known as an NP-hard problem, is critical to the recovery results. SCLR takes use of norm and nuclear norm as a convex surrogate function for norm and rank function. However, norm and nuclear norm cannot well approximate the norm and rank because there exist irreparable gaps between them. In this paper, an optimization model with norm and schatten- 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.