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A Non-convex Optimization Model for Signal Recovery

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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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Acknowledgements

The authors would like to express their gratitude to the anonymous referees as well as the Editor and Associate Editor for their valuable comments which lead to substantial improvements of the paper. This work was supported by China National Critical Project for Science and Technology on Water Pollution Prevention and Control, No. 2017ZX07104001, the Natural Science Fund for Colleges and Universities in Jiangsu Province, Grant No. 16KJB520014 and the Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology), Grant No. 30916014107.

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Authors and Affiliations

  1. College of Computer and Information, Hohai University, Nanjing, 210098, China

    Changwei Chen & Xiaofeng Zhou

  2. School of Information Engineering, Nanjing Xiaozhuang University, Nanjing, 210017, China

    Changwei Chen

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  1. Changwei Chen
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  2. Xiaofeng Zhou
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Correspondence to Xiaofeng Zhou.

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Chen, C., Zhou, X. A Non-convex Optimization Model for Signal Recovery. Neural Process Lett 54, 3529–3536 (2022). https://doi.org/10.1007/s11063-020-10253-4

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