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Interaction between noise and lesion modeling errors on EEG source localization accuracy

机译:EEG源定位精度噪声与病变建模误差的交互

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EEG dipole source reconstruction requires the assumption of a source model and of a conductive head model. Head-modeling errors and measurement noise in the EEG induce localization errors in the results of EEG source analysis. In this study effects of brain lesions on EEG dipole source localization have been investigated by computer simulation. We present a sensitivity study quantifying the effect on source localization accuracy of the interaction between the uncertainty in lesion conductivity assignment (LCA) and various levels of signal to noise ratio (SNR) in the EEGs. An inverse dipole fitting procedure, based on simulated noiseless EEG measurements and with SNR 5, 10 and 15, was carried out in 5 pathological situations, assuming an incorrect LCA ranging from a half to twice the real value. Incorrect LCA in noiseless conditions led to markedly wrong source reconstruction for high lesion conductivity values (localization errors up to 1,7 cm). We propose a method based on residual error analysis to improve lesion conductivity estimate. This procedure can identify lesion tissue conductivity with only a few percent error reducing the LE to values given by noise only.
机译:EEG偶极子源重建需要源模型的和导电的头部模型的假设。头建模在EEG误差和测量噪声诱发脑电图源分析的结果定位误差。在这项研究中脑电偶极子源定位脑部病变的影响已经通过计算机模拟研究。我们提出了一个灵敏度研究定量在病变电导率分配(LCA)和各种级别的信号的在脑电图噪声比(SNR)中的不确定性之间的相互作用的源定位精度的影响。基于模拟无噪声EEG测量并与SNR 5,第10和15的逆偶极拟合程序,在5种病理情况进行,假设一个不正确的LCA范围从半至两倍的实际价值。在导致显着错误的来源重建为高病变电导率值无噪声条件不正确LCA(定位误差高达1,7厘米)。我们提出了一种基于残差分析,以改善病变传导性估计的方法。此过程可以识别与只有百分之几误差减少LE由噪声仅给定的值病变组织的导电性。

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