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一项使用人体颅骨模型对脑磁图(MEG)和脑电图(EEG)偶极子定位准确性的研究。

A study of dipole localization accuracy for MEG and EEG using a human skull phantom.

作者信息

Leahy R M, Mosher J C, Spencer M E, Huang M X, Lewine J D

机构信息

Signal & Image Processing Institute, University of Southern California, Los Angeles 90089, USA.

出版信息

Electroencephalogr Clin Neurophysiol. 1998 Aug;107(2):159-73. doi: 10.1016/s0013-4694(98)00057-1.

Abstract

OBJECTIVE

To investigate the accuracy of forward and inverse techniques for EEG and MEG dipole localization.

DESIGN AND METHODS

A human skull phantom was constructed with brain, skull and scalp layers and realistic relative conductivities. Thirty two independent current dipoles were distributed within the 'brain' region and EEG and MEG data collected separately for each dipole. The true dipole locations and orientations and the morphology of the brain, skull and scalp layers were extracted from X-ray CT data. The location of each dipole was estimated from the EEG and MEG data using the R-MUSIC inverse method and forward models based on spherical and realistic head geometries. Additional computer simulations were performed to investigate the factors affecting localization accuracy.

RESULTS

Localization errors using the relatively simpler locally fitted sphere approach are only slightly greater than those using a BEM approach. The average localization error over the 32 dipoles was 7-8 mm for EEG and 3 mm for MEG.

CONCLUSION

The superior performance of MEG over EEG appears to be because the latter is more sensitive to errors in the forward model arising from simplifying assumptions concerning the conductivity of the skull, scalp and brain.

摘要

目的

研究脑电图(EEG)和脑磁图(MEG)偶极子定位的正演和反演技术的准确性。

设计与方法

构建一个具有脑、颅骨和头皮层以及实际相对电导率的人体颅骨模型。32个独立电流偶极子分布在“脑”区域内,针对每个偶极子分别采集EEG和MEG数据。从X射线CT数据中提取每个偶极子的真实位置、方向以及脑、颅骨和头皮层的形态。使用R-MUSIC反演方法和基于球形及实际头部几何形状的正演模型,从EEG和MEG数据中估计每个偶极子的位置。进行了额外的计算机模拟以研究影响定位准确性 的因素。

结果

使用相对简单的局部拟合球体方法的定位误差仅略大于使用边界元法(BEM)的误差。32个偶极子的平均定位误差,EEG为7 - 8毫米,MEG为3毫米。

结论

MEG相对于EEG的优越性能似乎是因为EEG对由于关于颅骨、头皮和脑电导率的简化假设而导致的正演模型误差更为敏感。

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