ArticlePloS one2024
Deep EEG source localization via EMD-based fMRI high spatial frequency.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Multimodal EEG-MRI Neuroimaging in Schizophrenia-A Systematic and Mechanistic Review.Journal of clinical medicine · 2026Review
- Increasing EEG electrode density improves decoding of visual categories and source localization: an exploratory ultra-high-density EEG study.Communications engineering · 2026Article
- Inferring neural sources from electroencephalography: foundations and frontiers.Journal of neural engineering · 2026Review
- Neural circuit mechanisms of epilepsy: Maintenance of homeostasis at the cellular, synaptic, and neurotransmitter levels.Neural regeneration research · 2026Article
- Task-state P300 and functional brain network abnormalities in adolescent major depressive disorder: a Stroop paradigm study.Frontiers in psychiatry · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain imaging with a high-spatiotemporal resolution is crucial for accurate brain-function mapping. Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are two popular neuroimaging modalities with complementary features that record brain function with high temporal and spatial resolution, respectively. One popular non-invasive way to obtain data with both high spatial and temporal resolutions is to combine the fMRI activation map and EEG data to improve the spatial resolution of the EEG source localization. However, using the whole fMRI map may cause spurious results for the EEG source localization, especially for deep brain regions. Considering the head's conductivity, deep regions' sources with low activity are unlikely to be detected by the EEG electrodes at the scalp. In this study, we use fMRI's high spatial-frequency component to identify the local high-intensity activations that are most likely to be captured by the EEG. The 3D Empirical Mode Decomposition (3D-EMD), a data-driven method, is used to decompose the fMRI map into its spatial-frequency components. Different validation measurements for EEG source localization show improved performance for the EEG inverse-modeling informed by the fMRI's high-frequency spatial component compared to the fMRI-informed EEG source-localization methods. The level of improvement varies depending on the voxels' intensity and their distribution. Our experimental results also support this conclusion.
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