Cited by 89EEG and Brain-Computer InterfacesDirection-of-Arrival Estimation TechniquesSparse and Compressive Sensing Techniques

Imaging brain source extent from EEG/MEG by means of an iteratively reweighted edge sparsity minimization (IRES) strategy

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Sohrabpour, Abbas, Lu, Yunfeng, Worrell, Gregory, He, Bin·2016·NeuroImage

Abstract

Estimating extended brain sources using EEG/MEG source imaging techniques is challenging. EEG and MEG have excellent temporal resolution at millisecond scale but their spatial resolution is limited due to the volume conduction effect. We have exploited sparse signal processing techniques in this study to impose sparsity on the underlying source and its transformation in other domains (mathematical domains, like spatial gradient). Using an iterative reweighting strategy to penalize locations that are less likely to contain any source, it is shown that the proposed iteratively reweighted edge sparsity minimization (IRES) strategy can provide reasonable information regarding the location and extent of the underlying sources. This approach is unique in the sense that it estimates extended sources without the need of subjectively thresholding the solution. The performance of IRES was evaluated in a series of computer simulations. Different parameters such as source location and signal-to-noise ratio were varied and the estimated results were compared to the targets using metrics such as localization error (LE), area under curve (AUC) and overlap between the estimated and simulated sources. It is shown that IRES provides extended solutions which not only localize the source but also provide estimation for the source extent. The performance of IRES was further tested in epileptic patients undergoing intracranial EEG (iEEG) recording for pre-surgical evaluation. IRES was applied to scalp EEGs during interictal spikes, and results were compared with iEEG and surgical resection outcome in the patients. The pilot clinical study results are promising and demonstrate a good concordance between noninvasive IRES source estimation with iEEG and surgical resection outcomes in the same patients. The proposed algorithm, i.e. IRES, estimates extended source solutions from scalp electromagnetic signals which provide relatively accurate information about the location and extent of the underlying source.

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Crossref · 10.1016/j.neuroimage.2016.05.064

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  • Sparsity is often assumed to avoid over-fitting in inverse problems and has increasing applications in signal and image processing.

    “Sparsity is often assumed to avoid over-fitting in inverse problems, and has increasing applications in signal and image processing (43, 50–54).”
  • Recent development in sparse electrophysiological source imaging algorithms suggests the capability of estimating both source locations and extents.

    “Recent development in sparse ESI algorithms suggests the capability of estimating both the source locations and extents (54), reflecting the spatio-temporal characteristics of underlying brain sources and dynamics.”

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References 93 · Cited by 89

Authors
Sohrabpour, A., Lu, Y., Worrell, G., & He, B.
Affiliations
  • University of Minnesota (US)
  • Mayo Clinic (US)
Corresponding author
He, Bin
Venue
NeuroImage
Publisher
Elsevier BV
Published
2016-11
Volume
142
Pages
27-42
ISSN
1053-8119 (ISSN-L)
OpenAlex
W2406007819
Cited by
89
References
93
Citations by year
2026: 3 · 2025: 8 · 2024: 13 · 2023: 7 · 2022: 7 · 2021: 8 · 2020: 7 · 2019: 10 · 2018: 12 · 2017: 12 · 2016: 2
FWCI
7.64
Type
journal article
Language
en
Open access
Locations
  • NeuroImage (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • NIH (EB006433, EY023101, HL117664)
  • NSF (CBET-1450956, CBET-1264782)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Physical Sciences › Computer Science › Signal Processing › Direction-of-Arrival Estimation Techniques
  • Physical Sciences › Engineering › Computational Mechanics › Sparse and Compressive Sensing Techniques
Keywords
EEG source localization, MEG source imaging, sparse signal processing, volume conduction, intracranial EEG, interictal spikes, surgical resection outcomes
MeSH terms
Brain, Electroencephalography, Humans, Models, Theoretical, Signal Processing, Computer-Assisted, Magnetoencephalography, Neuroimaging

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