A systematic review of EEG source localization techniques and their applications on diagnosis of brain abnormalities.

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  • Additional Information
    • Source:
      Publisher: Elsevier/North-Holland Biomedical Press Country of Publication: Netherlands NLM ID: 7905558 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-678X (Electronic) Linking ISSN: 01650270 NLM ISO Abbreviation: J Neurosci Methods Subsets: MEDLINE
    • Publication Information:
      Original Publication: Amsterdam, Elsevier/North-Holland Biomedical Press.
    • Subject Terms:
    • Abstract:
      In recent years, multiple noninvasive imaging modalities have been used to develop a better understanding of the human brain functionality, including positron emission tomography, single-photon emission computed tomography, and functional magnetic resonance imaging, all of which provide brain images with millimeter spatial resolutions. Despite good spatial resolution, time resolution of these methods are poor and values are about seconds. Scalp electroencephalography recordings can be used to perform the inverse problem in order to specify the location of the dominant sources of the brain activity. In this paper, EEG source localization method, diagnosis of brain abnormalities using common EEG source localization methods, investigating the effect of the head model on EEG source imaging results have been studied. In this review we present enough evidence that provides motivation for consideration in the future research using EEG source localization methods.
      Competing Interests: Declaration of Competing Interest None.
      (Copyright © 2020 Elsevier B.V. All rights reserved.)
    • Contributed Indexing:
      Keywords: EEG signals; brain abnormalities; head model; source localization; the inverse problem; time resolution
    • Publication Date:
      Date Created: 20200501 Date Completed: 20210621 Latest Revision: 20210621
    • Publication Date:
      20240628
    • Accession Number:
      10.1016/j.jneumeth.2020.108740
    • Accession Number:
      32353472