Magnetoencephalography (MEG) is a pivotal neuroimaging technique for diagnosing and treating brain disorders, known for its precise measurement of the brain’s magnetic fields due to electrical activity. Accurate brain source localization is essential for neurosurgical planning, but the MEG inverse problem—determining brain source locations from MEG data—is complex and inherently ill-posed. This article introduces a novel, data-driven approach to enhance MEG source localization and brain activity characterization. We compare three encoder models, VGGNet, ViT, and ResNet, assessing their performance across varying noise levels. Our findings reveal the effectiveness of neural networks in addressing challenging neuroimaging problems, underscoring their potential in advancing MEG applications.
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