Objective. Neonatal electroencephalography (EEG) source localization is highly prone to errors due to head modeling deficiencies. In this study, we investigated the effect of head model complexities on the accuracy of EEG source localization in full term neonates using a realistic volume conductor head model. Approach. We performed numerical simulations to investigate source localization errors caused by cerebrospinal fluid (CSF) and fontanel exclusion and gray matter (GM)/white matter (WM) distinction using the finite element method. Main results. Our results showed that the exclusion of CSF from the head model could cause significant localization errors mostly for sources closer to the inner surface of the skull. With a less pronounced effect compared to the CSF exclusion, the discrimination between GM and WM also widely affected all sources, especially those located in deeper structures. The exclusion of the fontanels from the head model led to source lo...

中文翻译：

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头部建模中结构复杂性对新生儿脑电图源定位准确性的影响。

客观的。由于头部建模缺陷，新生儿脑电图 (EEG) 源定位极易出错。在这项研究中，我们使用现实的体积导体头部模型研究了头部模型复杂性对足月新生儿脑电图源定位准确性的影响。方法。我们使用有限元方法进行了数值模拟，以研究由脑脊液 (CSF) 和囟门排斥以及灰质 (GM)/白质 (WM) 区分引起的源定位误差。主要结果。我们的结果表明，从头部模型中排除 CSF 可能会导致显着的定位错误，主要是针对靠近颅骨内表面的来源。与排除 CSF 相比，效果不太明显，GM 和 WM 之间的歧视也广泛影响了所有来源，尤其是位于更深结构中的那些。从头部模型中排除囟门导致了来源失...