精确脑电图和脑磁图的有损人体头部深源全波解决方案

Sattar Samadi, Bijan Zakeri, Reza Khanbabaie
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摘要

简介大脑中的电流在神经元内部流动,并穿过神经元的边界进入细胞外介质,形成电场和磁场。这些场包含大脑活动的适当信息,可通过脑电图(EEG)、脑磁图(MEG)和直接神经成像进行测量:本文采用神经元活动和人体头部的电磁模型,通过全波方法(即不做任何近似)推导出电场和磁场(脑电波)。目前,脑电波的推导只能使用电磁理论中麦克斯韦方程的准静态近似(QSA):因此,脑成像中的源定位会产生一些误差。迄今为止,尚未研究过 QSA 对电场和磁场输出结果的误差率。由于现代脑电图(EEG)和脑磁图(MEG)设备的灵敏度不断提高,这一问题变得更加明显。本研究介绍了 QSA 在此问题中遇到的问题,并揭示了全波求解的必要性。然后,首次以闭合形式提出了该问题的全波解决方案。该解决方案在两种情况下完成:源(有源神经元)位于球体中心,以及源位于球体中心以外但深入球体内部。第一种情况比较简单,但第二种情况要复杂得多,使用偏波数列表达式求解:该模型的重要成果之一是改进了对脑电图和脑动静脉图测量结果的解释,从而实现了更准确的声源定位。
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A Full-wave Solution of Deep Sources in the Lossy Human Head to Accurate Electroencephalography and Magnetoencephalography.

Introduction: Currents in the brain flow inside neurons and across their boundaries into the extracellular medium, create electric and magnetic fields. These fields, which contain suitable information on brain activity, can be measured by electroencephalography (EEG), magnetoencephalography (MEG), and direct neural imaging.

Methods: In this paper, we employed an electromagnetic model of the neuron activity and human head to derive electric and magnetic fields (brain waves) using a full-wave approach (ie. without any approximation). Currently, the brain waves are only derived using the quasi-static approximation (QSA) of Maxwell's equations in electromagnetic theory.

Results: As a result, source localization in brain imaging will produce some errors. So far, the error rate of the QSA on the output results of electric and magnetic fields has not been investigated. This issue has become more noticeable due to the increased sensitivity of modern electroencephalography (EEG) and magnetoencephalography (MEG) devices. This work introduces issues that QSA encounters in this problem and reveals the necessity of a full-wave solution. Then, a full-wave solution of the problem in closed-form format is presented for the first time. This solution is done in two scenarios: the source (active neurons) is in the center of a sphere, and when the source is out of the center but deeply inside the sphere. The first scenario is simpler, but the second one is much more complicated and is solved using a partial-wave series expression.

Conclusion: One of the significant achievements of this model is improving the interpretation of EEG and MEG measurements, resulting in more accurate source localization.

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