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Updated: Feb 13, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
Published on: June 27, 2013
Symmetric Fusion of fMRI and EEG for Spectrally Resolved Functional Neuroimaging
Jung-Hoon Kim1,2, Zhongming Liu3,4
1Developing Brain Institute, Children's National Hospital, Washington DC, USA.
None:
Simultaneous electroencephalography (EEG) and functional MRI (fMRI) offers complementary sensitivity to fast electrophysiological dynamics of EEG and spatially resolved hemodynamics of fMRI, yet previous joint-analysis approaches are confined to fixed task paradigms and struggle with continuous or naturalistic brain states. We FSINC (Fusing Source Imaging based on a Neurovascular Coupling) model, a unified EEG-fMRI source imaging framework that reconstructs cortical activity to simultaneously explain both modalities. FSINC integrates frequency-resolved EEG source activity with fMRI via a data-driven neurovascular coupling model that estimates band-specific coupling coefficients (β) and accommodates a tunable spatial-temporal trade-off through hyperparameters ( ). In realistic simulations, FSINC outperformed conventional methods (wMNE, LORETA) in both spatial and temporal accuracy across EEG SNRs (-10 to 10dB) and numbers of concurrent sources (up to five), with optimal performance at and (e.g., LE: 0.51±0.24mm; SDI: 0.03±0.37mm; temporal accuracy: 0.95 ± 0.05). Applied to simultaneous EEG-fMRI during contrast-reversing visual stimulation (=5.95Hz), FSINC revealed stimulus-locked responses localized to early visual cortex and stimulus-induced modulation of intrinsic alpha oscillations extending into visual and attention networks, patterns that conventional methods failed to capture. Estimated β-weights were broadly consistent with prior reports of negative (theta/alpha) and positive (gamma) BOLD-electrophysiology associations. These findings demonstrate that FSINC enables high-spatiotemporal-resolution source imaging from EEG-fMRI recordings via data-driven hemodynamic modelling, and is expected to be well-suited for continuous and naturalistic brain states (e.g., resting state, natural moving-watching, and narrative listening) that are difficult to interrogate with either modality alone.
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