Efficient Dipole Parameter Estimation in EEG Systems With Near-ML Performance
Efficient Dipole Parameter Estimation in EEG Systems With Near-ML Performance
复制标题
具有接近机器学习性能的脑电图系统中的高效偶极子参数估计
DOI：
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发表时间：
2012
影响因子：
4.6
通讯作者：
Z. Nenadic
中科院分区：
文献类型：
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作者：
Shun;A. L. Swindlehurst;Po T. Wang;Z. Nenadic
具有强时间相关性的源信号可能对高分辨率脑电图源定位算法提出挑战。在本文中，我们提出了两种方法，它们能够在其他高分辨率方法（例如多信号分类和线性约束最小方差波束形成）失败的情况下准确定位高度相关的源。这些方法基于最佳最大似然 (ML) 方法的近似，但当除了源位置之外还需要估计等效 EEG 偶极子方向和力矩时，它们比 ML 具有显着的计算优势。第一种方法使用两阶段方法，其中假设非结构化偶极矩模型来执行定位，然后通过在第二步中使用这些估计来获得偶极子方向。第二种方法基于噪声子空间拟合概念的使用，并且已被证明提供与直接 ML 方法渐近等效的性能。由于源位置和偶极矩的估计是解耦的，这两种技术都比 ML 更简单地进行优化。给出了使用模拟和听觉实验数据的示例来说明算法的性能。
Source signals that have strong temporal correlation can pose a challenge for high-resolution EEG source localization algorithms. In this paper, we present two methods that are able to accurately locate highly correlated sources in situations where other high-resolution methods such as multiple signal classification and linearly constrained minimum variance beamforming fail. These methods are based on approximations to the optimal maximum likelihood (ML) approach, but offer significant computational advantages over ML when estimates of the equivalent EEG dipole orientation and moment are required in addition to the source location. The first method uses a two-stage approach in which localization is performed assuming an unstructured dipole moment model, and then the dipole orientation is obtained by using these estimates in a second step. The second method is based on the use of the noise subspace fitting concept, and has been shown to provide performance that is asymptotically equivalent to the direct ML method. Both techniques lead to a considerably simpler optimization than ML since the estimation of the source locations and dipole moments is decoupled. Examples using data from simulations and auditory experiments are presented to illustrate the performance of the algorithms.