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Neural Processing Letters

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19-05-2020

A Non-convex Optimization Model for Signal Recovery

Authors: Changwei Chen, Xiaofeng Zhou

Published in: Neural Processing Letters | Issue 5/2022

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Abstract

The electroencephalogram (EEG) signal is one of the most frequently used biomedical signals. In order to accurately exploit the cosparsity and low-rank property which is nature in multichannel EEG signals, motivated by the fact that weighted schatten-p norm and \({l_q}\) norm can better approximate the matrix rank and \({l_0}\) norm, in this paper, a non-convex optimization model is proposed to precisely reconstruct the multichannel EEG signal. weighted schatten-p norm and \({l_q}\) norm are used to enforce low-rank property and cosparsity. In addition, an efficient iterative optimization method based on alternating direction method of multipliers is used to solve the resulting non-convex 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 signals.

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Title: A Non-convex Optimization Model for Signal Recovery
Authors: Changwei Chen
Xiaofeng Zhou
Publication date: 19-05-2020
Publisher: Springer US
Published in: Neural Processing Letters / Issue 5/2022
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-020-10253-4

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