Advancing EEG/MEG Source Imaging with Geometric-Informed Basis Functions
arxiv(2024)
摘要
Electroencephalography (EEG) and Magnetoencephalography (MEG) are pivotal in
understanding brain activity but are limited by their poor spatial resolution.
EEG/MEG source imaging (ESI) infers the high-resolution electric field
distribution in the brain based on the low-resolution scalp EEG/MEG
observations. However, the ESI problem is ill-posed, and how to bring
neuroscience priors into ESI method is the key. Here, we present a novel method
which utilizes the Brain Geometric-informed Basis Functions (GBFs) as priors to
enhance EEG/MEG source imaging. Through comprehensive experiments on both
synthetic data and real task EEG data, we demonstrate the superiority of GBFs
over traditional spatial basis functions (e.g., Harmonic and MSP), as well as
existing ESI methods (e.g., dSPM, MNE, sLORETA, eLORETA). GBFs provide robust
ESI results under different noise levels, and result in biologically
interpretable EEG sources. We believe the high-resolution EEG source imaging
from GBFs will greatly advance neuroscience research.
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