脑电图扫描仪对成像技术、正向模型计算方法和阵列维度的依赖性评估。
An evaluation of EEG scanner's dependence on the imaging technique, forward model computation method, and array dimensionality.
作者信息
Stahlhut Carsten, Attias Hagai Thomas, Stopczynski Arkadiusz, Petersen Michael Kai, Larsen Jakob Eg, Hansen Lars Kai
机构信息
DTU Informatics, Technical University of Denmark, DK-2800 Kgs. Lyngby. C. Stahlhut.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1538-41. doi: 10.1109/EMBC.2012.6346235.
EEG source reconstruction involves solving an inverse problem that is highly ill-posed and dependent on a generally fixed forward propagation model. In this contribution we compare a low and high density EEG setup's dependence on correct forward modeling. Specifically, we examine how different forward models affect the source estimates obtained using four inverse solvers Minimum-Norm, LORETA, Minimum-Variance Adaptive Beamformer, and Sparse Bayesian Learning.
脑电图源重建涉及解决一个严重不适定且依赖于通常固定的正向传播模型的逆问题。在本论文中，我们比较了低密度和高密度脑电图设置对正确正向建模的依赖性。具体而言，我们研究了不同的正向模型如何影响使用四种逆解算器（最小范数、LORETA、最小方差自适应波束形成器和稀疏贝叶斯学习）获得的源估计。
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