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

    10 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: 7 Dec 20189 Dec 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
    Period7/12/189/12/18

    Keywords

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

    Fingerprint

    Dive into the research topics of 'Estimating latent brain sources with low-rank representation and graph regularization'. Together they form a unique fingerprint.

    Cite this