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Updated: May 24, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
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Advancing EEG/MEG Source Imaging with Geometric-Informed Basis Functions.

Song Wang, Chen Wei, Kexin Lou

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    Summary
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    This study introduces Brain Geometric-informed Basis Functions (GBFs) to improve electroencephalography (EEG) and magnetoencephalography (MEG) source imaging. GBFs offer superior spatial resolution and biologically interpretable results for brain activity analysis.

    Area of Science:

    • Neuroscience
    • Biophysics
    • Medical Imaging

    Background:

    • Electroencephalography (EEG) and Magnetoencephalography (MEG) are crucial for brain activity research but suffer from low spatial resolution.
    • EEG/MEG source imaging (ESI) aims to reconstruct high-resolution brain activity from scalp recordings, but the ill-posed nature of the problem necessitates effective prior information.

    Purpose of the Study:

    • To introduce a novel EEG/MEG source imaging method using Brain Geometric-informed Basis Functions (GBFs) as neuroscience priors.
    • To enhance the spatial resolution and biological interpretability of ESI.

    Main Methods:

    • Development and application of Brain Geometric-informed Basis Functions (GBFs) for ESI.
    • Comprehensive validation using synthetic data and real task EEG data.

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    Last Updated: May 24, 2025

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  • Comparison with traditional spatial basis functions (Harmonic, MSP) and existing ESI methods (dSPM, MNE, sLORETA, eLORETA).

    • Main Results:

    • GBFs significantly outperform traditional methods and existing ESI techniques in terms of spatial resolution and accuracy.
    • The proposed GBF method demonstrates robustness across varying noise levels.
    • ESI results obtained using GBFs are biologically interpretable, aligning with neuroscience knowledge.

    Conclusions:

    • Brain Geometric-informed Basis Functions (GBFs) represent a significant advancement in EEG/MEG source imaging.
    • This novel approach enhances the capability to achieve high-resolution brain source imaging.
    • GBFs are expected to greatly facilitate and advance neuroscience research by providing more accurate and interpretable brain activity data.