ArticleNeuroImage2016
Imaging brain source extent from EEG/MEG by means of an iteratively reweighted edge sparsity minimization (IRES) strategy.
Article in NeuroImage, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Tactile contribution extends beyond exteroception during spatially guided finger movements.Scientific reports · 2025Article
- Stereo-EEG propagating source reconstruction identifies new surgical targets for epilepsy patients.Brain : a journal of neurology · 2025Article
- EEG dynamic source imaging using a regularized optimization with spatio-temporal constraints.Medical & biological engineering & computing · 2024Article
- XDL-ESI: Electrophysiological Sources Imaging via explainable deep learning framework with validation on simultaneous EEG and iEEG.NeuroImage · 2024Article
- Rejuvenating classical brain electrophysiology source localization methods with spatial graph Fourier filters for source extents estimation.Brain informatics · 2024Article
- Multi-Modal Electrophysiological Source Imaging With Attention Neural Networks Based on Deep Fusion of EEG and MEG.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2024Article
- Deep learning based source imaging provides strong sublobar localization of epileptogenic zone from MEG interictal spikes.NeuroImage · 2023Article
- Repairing Artifacts in Neural Activity Recordings Using Low-Rank Matrix Estimation.Sensors (Basel, Switzerland) · 2023Article
- Ideal current dipoles are appropriate source representations for simulating neurons for intracranial recordings.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2023Article
- From theory to practical fundamentals of electroencephalographic source imaging in localizing the epileptogenic zone.Epilepsia · 2022Review
- Imaging the extent and location of spatiotemporally distributed epileptiform sources from MEG measurements.NeuroImage. Clinical · 2022Article
- Exploring the extent of source imaging: Recent advances in noninvasive electromagnetic brain imaging.Current opinion in biomedical engineering · 2021Article
- Noninvasive high-frequency oscillations riding spikes delineates epileptogenic sources.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Accuracy and spatial properties of distributed magnetic source imaging techniques in the investigation of focal epilepsy patients.Human brain mapping · 2020Article
- Noninvasive electromagnetic source imaging of spatiotemporally distributed epileptogenic brain sources.Nature communications · 2020Article
- Robust Empirical Bayesian Reconstruction of Distributed Sources for Electromagnetic Brain Imaging.IEEE transactions on medical imaging · 2020Article
- EEGSourceSim: A framework for realistic simulation of EEG scalp data using MRI-based forward models and biologically plausible signals and noise.Journal of neuroscience methods · 2019Article
- Multi-Resolution Graph Based Volumetric Cortical Basis Functions From Local Anatomic Features.IEEE transactions on bio-medical engineering · 2019Article
- Electrophysiological Brain Connectivity: Theory and Implementation.IEEE transactions on bio-medical engineering · 2019Article
- Mapping electrical properties heterogeneity of tumor using boundary informed electrical properties tomography (BIEPT) at 7T.Magnetic resonance in medicine · 2019Article
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Authors and funding
4 authors.
Funding
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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