Extended Electrophysiological Source Imaging with Spatial Graph Filters

Feng Liu, Guihong Wan, Yevgeniy R. Semenov, Patrick L. Purdon

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

    3 Scopus citations

    Abstract

    Electrophysiological Source Imaging (ESI) refers to the process of localizing the brain source activation patterns given measured Electroencephalography (EEG) or Magnetoencephalography (MEG) signal from the scalp. Recent studies have focused on designing sophisticated neurophysiologically plausible regularizations or efficient estimation frameworks to solve the ESI problem, with the underlying assumption that brain source activation has some specific structures. Estimation of both source location and its extents is important in clinical applications. However, estimating the high dimensional extended location is challenging due to the highly coherent columns in the leadfield matrix, resulting in a reconstructed spiky spurious sources. In this work, we describe an efficient and accurate framework by exploiting the graph structure defined in the 3D mesh of the brain. Specifically, we decompose the graph signal representation in the source space into low-, medium-, and high-frequency subspaces, and project the source signal into the graph low-frequency subspace. We further introduce a low-rank representation with temporal graph regularization in the projected space to build the ESI framework, which can be efficiently solved. Experiments with simulated data and real world EEG data demonstrated the superiority of the proposed paradigm for estimating brain source extents.

    Original languageEnglish
    Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
    EditorsLinwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li
    Pages99-109
    Number of pages11
    DOIs
    StatePublished - 2022
    Event25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore
    Duration: 18 Sep 202222 Sep 2022

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume13431 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
    Country/TerritorySingapore
    CitySingapore
    Period18/09/2222/09/22

    Keywords

    • EEG/MEG source imaging
    • Graph signal processing
    • Low-rank representation
    • Spatial graph filter

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