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Reduction of the impact of multiple uncertain conductivity values on EEG dipole source analysis

机译:减少多个不确定电导率值对EEG偶极子源分析的影响

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It is well known that the uncertain knowledge of the conductivity values of the head tissues has an important impact upon the accuracy of the electroencephalogram source reconstruction. Assuming a certain value of the conductivity often leads to high reconstruction error values when solving the inverse problem. It is possible to quantify the impact of multiple uncertain conductivity values on the localization accuracy. We propose an approach that reduces the impact of these multiple uncertainties on the reconstruction accuracy of the dipole parameters. This paper elaborates the numerical method and shows results of localization accuracy in a five-shell spherical head model. Sensitivity analysis, when considering multiple layers in the head model, shows the different scales of the influence of the various uncertain conductivity values on the potential values. We propose a cost function that reduces the impact of multiple uncertainties of the conductivity value on the electroencephalogram dipole reconstruction and two strategies for selecting potential values on the basis of the sensitivity analysis. Numerical simulations, when considering multiple uncertainties in the model, provide results with higher reconstruction accuracy compared with the case where only a single uncertainty is taken into account.
机译:众所周知,头部组织的电导率值的不确定性对脑电图源重建的准确性有重要影响。假定电导率的某个值通常会在解决反问题时导致较高的重构误差值。可以量化多个不确定的电导率值对定位精度的影响。我们提出了一种减少这些多重不确定性对偶极子参数重建精度的影响的方法。本文阐述了数值方法,并显示了在五壳球头模型中定位精度的结果。当在头部模型中考虑多层时,灵敏度分析显示了各种不确定的电导率值对电势值的影响的不同尺度。我们提出了一种成本函数,该函数可减少电导率值的多个不确定性对脑电图偶极子重建的影响,并提出两种基于灵敏度分析选择电位值的策略。与仅考虑单个不确定性的情况相比,当考虑模型中的多个不确定性时,数值模拟可提供具有更高重构精度的结果。

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