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Estimating Latent Brain Sources with Low-Rank Representation and Graph Regularization

  • Feng Liu
  • Shouyi Wang
  • Jing Qin
  • Yifei Lou
  • Jay Rosenberger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11309)

Abstract

To infer latent brain source activation patterns under different cognitive tasks is an integral step to understand how our brain works. Traditional electroencephalogram (EEG) Source Imaging (ESI) methods usually do not distinguish task-related and spurious non-task-related sources that jointly generate EEG signals, which inevitably yield misleading reconstructed activation patterns. In this research, we assume that the task-related source signal intrinsically has a low-rank property, which is exploited to infer the true task-related EEG sources location. Although the true task-related source signal is sparse and low-rank, the contribution of spurious sources scattering over the source space with intermittent activation patterns makes the actual source space lose the low-rank property. To reconstruct a low-rank true source, we propose a novel ESI model that involves a spatial low-rank representation and a temporal Laplacian graph regularization, the latter of which guarantees the temporal smoothness of the source signal and eliminate the spurious ones. To solve the proposed model, an augmented Lagrangian objective function is formulated and an algorithm in the framework of alternating direction method of multipliers (ADMM) is proposed. Numerical results illustrate the effectivenesks of the proposed method in terms of reconstruction accuracy with high efficiency.

Keywords

EEG Source Imaging Low rank representation Graph Regularization Alternating direction method of multiplier (ADMM) 

Notes

Acknowledgment

This work has been partially supported by the NSF funding under grant number CMMI-1537504 and DMS-1522786. The research of Jing Qin is supported by the NSF grant DMS-1818374.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Feng Liu
    • 1
    • 2
  • Shouyi Wang
    • 3
  • Jing Qin
    • 4
  • Yifei Lou
    • 5
  • Jay Rosenberger
    • 3
  1. 1.Massachusetts General HospitalHarvard Medical SchoolBostonUSA
  2. 2.Picower Institue of Learning and MemoryMITCambridgeUSA
  3. 3.Department of Industrial EngineeringUniversity of Texas at ArlingtonArlingtonUSA
  4. 4.Department of Mathematical SciencesMontana State UniversityBozemanUSA
  5. 5.Department of Mathematical SciencesUniversity of Texas at DallasRichardsonUSA

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