ArticleIEEE transactions on medical imaging2020
Robust Empirical Bayesian Reconstruction of Distributed Sources for Electromagnetic Brain Imaging.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Efficient Super-Resolution Bayesian Eletromagnetic Brain Imaging.IEEE transactions on bio-medical engineering · 2026Article
- Flexible Alternating Projection for Spatially Extended Brain Source Localization.IEEE transactions on bio-medical engineering · 2025Article
- Robust interpolation of EEG/MEG sensor time-series via electromagnetic source imaging.Journal of neural engineering · 2025Article
- Structured noise champagne: an empirical Bayesian algorithm for electromagnetic brain imaging with structured noise.Frontiers in human neuroscience · 2025Article
- 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
- Joint Learning of Full-Structure Noise in Hierarchical Bayesian Regression Models.IEEE transactions on medical imaging · 2024Article
- Deep learning based source imaging provides strong sublobar localization of epileptogenic zone from MEG interictal spikes.NeuroImage · 2023Article
- Empirical Bayesian localization of event-related time-frequency neural activity dynamics.NeuroImage · 2022Article
- Imaging somatosensory cortex responses measured by OPM-MEG: Variational free energy-based spatial smoothing estimation approach.iScience · 2022Article
- Exploring the extent of source imaging: Recent advances in noninvasive electromagnetic brain imaging.Current opinion in biomedical engineering · 2021Article
- Robust estimation of noise for electromagnetic brain imaging with the champagne algorithm.NeuroImage · 2021Article
- Clinical Validation of the Champagne Algorithm for Epilepsy Spike Localization.Frontiers in human neuroscience · 2021Article
- A Novel Bayesian Approach for EEG Source Localization.Computational intelligence and neuroscience · 2020Article
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5 authors.
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Abstract
Electromagnetic brain imaging is the reconstruction of brain activity from non-invasive recordings of the magnetic fields and electric potentials. An enduring challenge in this imaging modality is estimating the number, location, and time course of sources, especially for the reconstruction of distributed brain sources with complex spatial extent. Here, we introduce a novel robust empirical Bayesian algorithm that enables better reconstruction of distributed brain source activity with two key ideas: kernel smoothing and hyperparameter tiling. Since the proposed algorithm builds upon many of the performance features of the sparse source reconstruction algorithm - Champagne and we refer to this algorithm as Smooth Champagne. Smooth Champagne is robust to the effects of high levels of noise, interference, and highly correlated brain source activity. Simulations demonstrate excellent performance of Smooth Champagne when compared to benchmark algorithms in accurately determining the spatial extent of distributed source activity. Smooth Champagne also accurately reconstructs real MEG and EEG data.
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