Inserm, UMR 642, Campus de Beaulieu, Rennes, F-35042, France.
Neuroimage. 2011 May 1;56(1):102-13. doi: 10.1016/j.neuroimage.2011.01.054. Epub 2011 Jan 27.
We propose a new MUSIC-like method, called 2q-ExSo-MUSIC (q ≥ 1). This method is an extension of the 2q-MUSIC (q ≥ 1) approach for solving the EEG/MEG inverse problem, when spatially-extended neocortical sources ("ExSo") are considered. It introduces a novel ExSo-MUSIC principle. The novelty is two-fold: i) the parameterization of the spatial source distribution that leads to an appropriate metric in the context of distributed brain sources and ii) the introduction of an original, efficient and low-cost way of optimizing this metric. In 2q-ExSo-MUSIC, the possible use of higher order statistics (q ≥ 2) offers a better robustness with respect to Gaussian noise of unknown spatial coherence and modeling errors. As a result we reduced the penalizing effects of both the background cerebral activity that can be seen as a Gaussian and spatially correlated noise, and the modeling errors induced by the non-exact resolution of the forward problem. Computer results on simulated EEG signals obtained with physiologically-relevant models of both the sources and the volume conductor show a highly increased performance of our 2q-ExSo-MUSIC method as compared to the classical 2q-MUSIC algorithms.
我们提出了一种新的类似于 MUSIC 的方法，称为 2q-ExSo-MUSIC（q≥1）。当考虑空间扩展的新皮层源（“ExSo”）时，该方法是解决 EEG/MEG 逆问题的 2q-MUSIC（q≥1）方法的扩展。它引入了一种新的 ExSo-MUSIC 原理。新颖之处在于二：i）空间源分布的参数化，导致在分布式脑源的背景下适当的度量，ii）优化该度量的原始、高效和低成本方法的引入。在 2q-ExSo-MUSIC 中，更高阶统计量（q≥2）的使用提供了对未知空间相干性和建模误差的高斯噪声的更好鲁棒性。结果，我们降低了背景大脑活动（可以看作是高斯和空间相关噪声）以及正向问题的不精确分辨率引起的建模误差的惩罚效应。使用源和体积导体的生理相关模型获得的模拟 EEG 信号的计算机结果表明，与经典的 2q-MUSIC 算法相比，我们的 2q-ExSo-MUSIC 方法的性能有了很大提高。
