Premium
An enhanced weighted greedy analysis pursuit algorithm with application to EEG signal reconstruction
Author(s) -
Mohagheghian Fahimeh,
Deevband Mohammad Reza,
Samadzadehaghdam Nasser,
Khajehpour Hassan,
Makkiabadi Bahador
Publication year - 2020
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22438
Subject(s) - matching pursuit , compressed sensing , computer science , robustness (evolution) , greedy algorithm , signal reconstruction , algorithm , subspace topology , constraint (computer aided design) , signal (programming language) , signal processing , signal compression , decoupling (probability) , basis pursuit , mathematical optimization , pattern recognition (psychology) , artificial intelligence , mathematics , telecommunications , programming language , biochemistry , chemistry , radar , geometry , control engineering , engineering , gene
In the past decade, compressed sensing (CS) has provided an efficient framework for signal compression and recovery as the intermediate steps in signal processing. The well‐known greedy analysis algorithm, called Greedy Analysis Pursuit (GAP) has the capability of recovering the signals from a restricted number of measurements. In this article, we propose an extension to the GAP to solve the weighted optimization problem satisfying an inequality constraint based on the Lorentzian cost function to modify the EEG signal reconstruction in the presence of heavy‐tailed impulsive noise. Numerical results illustrate the effectiveness of our proposed algorithm, called enhanced weighted GAP (ewGAP) to reinforce the efficiency of the signal reconstruction and provide an appropriate candidate for compressed sensing of the EEG signals. The suggested algorithm achieves promising reconstruction performance and robustness that outperforms other analysis‐based approaches such as GAP, Analysis Subspace Pursuit (ASP), and Analysis Compressive Sampling Matching Pursuit (ACoSaMP).