Rejuvenating Classical Source Localization Methods with Spatial Graph Filters

Shihao Yang, Meng Jiao, Jing Xiang, Daphne Kalkanis, Hai Sun, Feng Liu

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

    1 Scopus citations

    Abstract

    EEG/MEG source imaging (ESI) aims to find the underlying brain sources to explain the observed EEG or MEG measurement. Multiple classical approaches have been proposed to solve the ESI problem based on different neurophysiological assumptions. To support the clinical decision making, it is important to estimate not only the exact location of source signal but also the boundary of extended source activation. Traditional methods usually render over-diffuse or sparse solution, which limits the source extent estimation accuracy. In this work, we exploit the graph structure defined in the 3D mesh of the brain by decomposing the spatial graph signal into low-, medium-, and high-frequency sub-spaces, and leverage the low frequency components of graph Fourier basis to approximate the extended region of source activation. We integrate the classical source localization methods with the low frequency subspace components derived from the spatial graph signal. The proposed method can effectively reconstruct focal extent patterns and significantly improve the performance compared to classical algorithms through both synthetic data and real EEG data.

    Original languageEnglish
    Title of host publicationBrain Informatics - 16th International Conference, BI 2023, Proceedings
    EditorsFeng Liu, Hongjun Wang, Yu Zhang, Hongzhi Kuai, Emily P. Stephen
    Pages286-296
    Number of pages11
    DOIs
    StatePublished - 2023
    Event16th International Conference on Brain Informatics, BI 2023 - Hoboken, United States
    Duration: 1 Aug 20233 Aug 2023

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume13974 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference16th International Conference on Brain Informatics, BI 2023
    Country/TerritoryUnited States
    CityHoboken
    Period1/08/233/08/23

    Keywords

    • EEG/MEG Source Imaging
    • Graph Signal Processing
    • Inverse Problem
    • Spatial Graph Filter

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