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MEG Signal Reconstruction via Low-Rank Matrix Recovery for Source Imaging in Simulations

  • Yegang Hu
  • Jicong ZhangEmail author
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 810)

Abstract

Source imaging with magnetoencephalography (MEG) has obtained good spatial accuracy on the shallow sources, and has been successfully applied in the brain cognition and the diagnosis of brain disease. However, its utility with locating deep sources may be more challenging. In this study, a new source imaging method was proposed to find real brain activity on deep locations. A sensor array with MEG measurements including 306 channels was represented as a low-rank matrix plus sparse noises. The low-rank matrix was used to reconstruct the MEG signal and remove interference. The source model was estimated using the reconstructed MEG signal and minimum variance beamforming. Simulations with a realistic head model indicated that the proposed method was effective.

Keywords

Source imaging Magnetoencephalography (MEG) Low-rank matrix recovery Beamforming Signal reconstruction 

Notes

Acknowledgements

This work was supported by the National Key R&D Program of China (Grant Number: 2016YFF0201002), the National Natural Science Foundation of China (Grant Numbers: 61301005, 61572055), the Beihang University Hefei Innovation Research Institute, Project of ‘The Thousand Talents Plan’ for Young Professionals, and ‘The Thousand Talents Plan’ Workstation between Beihang University and Jiangsu Yuwell Medical Equipment and Supply Co. Ltd.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
  2. 2.Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang UniversityBeijingChina
  3. 3.Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang UniversityBeijingChina
  4. 4.Hefei Innovation Research Institute, Beihang UniversityHefeiChina

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