Estimating latent brain sources with low-rank representation and graph regularization

Feng Liu, Shouyi Wang, Jing Qin, Yifei Lou, Jay Rosenberger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

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.

Original languageEnglish
Title of host publicationBrain Informatics - International Conference, BI 2018, Proceedings
EditorsYang Yang, Vicky Yamamoto, Shouyi Wang, Erick Jones, Jianzhong Su, Tom Mitchell, Leon Iasemidis
Pages304-316
Number of pages13
DOIs
StatePublished - 2018
EventInternational Conference on Brain Informatics, BI 2018 - Arlington, United States
Duration: Dec 7 2018Dec 9 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11309 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Brain Informatics, BI 2018
Country/TerritoryUnited States
CityArlington
Period12/7/1812/9/18

Bibliographical note

Funding Information:
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.

Funding Information:
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.

Publisher Copyright:
© 2018, Springer Nature Switzerland AG.

Keywords

  • Alternating direction method of multiplier (ADMM)
  • EEG source imaging
  • Graph regularization
  • Low rank representation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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