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Modeling brain electrical activity by an image-based boundary element method

机译:通过基于图像的边界元方法对脑电活动进行建模

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Electroencephalography (EEC) source localization of brain activity is of high diagnostic value. This work aims to improve the low-spatial-resolution scalp EEC measurement through noninvasive numerical procedures. An image-based boundary element method (BEM) is developed to reconstruct cortical brain potential distribution from the scalp input The developed BEM circumvents a practical challenge of linking scan images to model-based computation by translating the scan surface tessellation directly into mesh discretization for the BEM. Related issues, such as the numerical regularization of the ill-posed inverse problem, which are crucial to achieving reliable solutions, are discussed. The numerical studies show that the developed BEM can effectively handle the potential reconstruction on a detailed brain surface from blurry scalp potential input, and may become a promising tool to aid clinical diagnosis of brain-related problems.
机译:脑活动的脑电图(EEC)源定位具有很高的诊断价值。这项工作旨在通过无创数值程序改善低空间分辨率头皮EEC测量。开发了一种基于图像的边界元方法(BEM),以从头皮输入重建皮质脑电势分布。开发的BEM通过将扫描表面的镶嵌细分直接转换为网格离散化,从而克服了将扫描图像链接到基于模型的计算的实际挑战。 BEM。讨论了相关问题,例如不适定反问题的数值正则化,这对于实现可靠的解决方案至关重要。数值研究表明,开发的BEM可以有效地处理来自头皮电势输入模糊的详细大脑表面的电势重建,并且可能成为有助于临床诊断脑相关问题的有前途的工具。

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