Electroencephalographic (EEG) source imaging (ESI) is a powerful method for studying brain functions and surgical resection of epileptic foci. However, accurately estimating the location and extent of brain sources remains challenging due to noise and background interference in EEG signals. To reconstruct extended brain sources, we propose a new ESI method called Variation Sparse Source Imaging based on Generalized Gaussian Distribution (VSSI-GGD). VSSI-GGD uses the generalized Gaussian prior as a sparse constraint on the spatial variation domain and embeds it into the Bayesian framework for source estimation. Using a variational technique, we approximate the intractable true posterior with a Gaussian density. Through convex analysis, the Bayesian inference problem is transformed entirely into a series of regularized ${L}_{{2}{p}}$ -norm ( ${0} < {p} < {1}$ ) optimization problems, which are efficiently solved with the ADMM algorithm. Imaging results of numerical simulations and human experimental dataset analysis reveal the superior performance of VSSI-GGD, which provides higher spatial resolution with clear boundaries compared to benchmark algorithms. VSSI-GGD can potentially serve as an effective and robust spatiotemporal EEG source imaging method. The source code of VSSI-GGD is available at https://github.com/Mashirops/VSSI-GGD.git .

中文翻译：

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VSSI-GGD：一种基于广义高斯分布的变稀疏脑电源成像方法

脑电图 (EEG) 源成像 (ESI) 是研究大脑功能和手术切除癫痫病灶的有效方法。然而，由于脑电图信号中的噪声和背景干扰，准确估计脑源的位置和范围仍然具有挑战性。为了重建扩展的脑源，我们提出了一种新的 ESI 方法，称为基于广义高斯分布的变异稀疏源成像 (VSSI-GGD)。 VSSI-GGD使用广义高斯先验作为空间变化域的稀疏约束，并将其嵌入到贝叶斯框架中进行源估计。使用变分技术，我们用高斯密度来近似棘手的真实后验。通过凸分析，贝叶斯推理问题完全转化为一系列正则化的问题 ${L}_{{2}{p}}$ -范数（ ${0} < {p} < {1}$ ）优化问题，可以使用 ADMM 算法有效解决。数值模拟和人体实验数据集分析的成像结果揭示了 VSSI-GGD 的优越性能，与基准算法相比，它提供了更高的空间分辨率和清晰的边界。 VSSI-GGD 可以作为一种有效且稳健的时空脑电图源成像方法。 VSSI-GGD 的源代码位于https://github.com/Mashirops/VSSI-GGD.git 。