EEG/MEG source imaging (ESI) aims to find the underlying brain sources to explain the observed EEG or MEG measurement. Multiple classical approaches have been proposed to solve the ESI problem based on different neurophysiological assumptions. To support clinical decision-making, it is important to estimate not only the exact location of the source signal but also the extended source activation regions. Existing methods may render over-diffuse or sparse solutions, which limit the source extent estimation accuracy. In this work, we leverage the graph structures defined in the 3D mesh of the brain and the spatial graph Fourier transform (GFT) to decompose the spatial graph structure into sub-spaces of low-, medium-, and high-frequency basis. We propose to use the low-frequency basis of spatial graph filters to approximate the extended areas of brain activation and embed the GFT into the classical ESI methods. We validated the classical source localization methods with the corresponding improved version using GFT in both synthetic data and real data. We found the proposed method can effectively reconstruct focal source patterns and significantly improve the performance compared to the classical algorithms.

中文翻译：

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使用空间图傅里叶滤波器复兴经典脑电生理学源定位方法以进行源范围估计

EEG/MEG 源成像 (ESI) 旨在寻找潜在的脑源来解释观察到的 EEG 或 MEG 测量结果。基于不同的神经生理学假设，多种经典方法被提出来解决 ESI 问题。为了支持临床决策，不仅要估计源信号的确切位置，还要估计扩展的源激活区域。现有方法可能会产生过度扩散或稀疏的解决方案，这限制了源范围估计的准确性。在这项工作中，我们利用大脑 3D 网格中定义的图结构和空间图傅里叶变换（GFT）将空间图结构分解为低频、中频和高频基础的子空间。我们建议使用空间图滤波器的低频基础来近似大脑激活的扩展区域，并将 GFT 嵌入到经典的 ESI 方法中。我们在合成数据和真实数据中使用 GFT 验证了经典的源定位方法以及相应的改进版本。我们发现所提出的方法可以有效地重建焦点源模式，并且与经典算法相比显着提高性能。