Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697, USA.
IEEE Trans Biomed Eng. 2013 Aug;60(8):2280-8. doi: 10.1109/TBME.2013.2253101. Epub 2013 Mar 15.
In this paper, we propose novel matching pursuit (MP)-based algorithms for EEG/MEG dipole source localization and parameter estimation for multiple measurement vectors with constant sparsity. The algorithms combine the ideas of MP for sparse signal recovery and source deflation, as employed in estimation via alternating projections. The source-deflated matching pursuit (SDMP) approach mitigates the problem of residual interference inherent in sequential MP-based methods or recursively applied (RAP)-MUSIC. Furthermore, unlike prior methods based on alternating projection, SDMP allows one to efficiently estimate the dipole orientation in addition to its location. Simulations show that the proposed algorithms outperform existing techniques under various conditions, including those with highly correlated sources. Results using real EEG data from auditory experiments are also presented to illustrate the performance of these algorithms.
在本文中，我们提出了新颖的匹配 pursuit（MP）算法，用于 EEG/MEG 偶极子源定位和多个测量向量的参数估计，这些向量具有恒定的稀疏性。这些算法结合了 MP 用于稀疏信号恢复和源排空的思想，这些思想用于交替投影估计。源排空匹配 pursuit（SDMP）方法减轻了顺序 MP 方法或递归应用（RAP）-MUSIC 中固有的残余干扰问题。此外，与基于交替投影的先前方法不同，SDMP 允许除位置外还可以有效地估计偶极子方向。仿真结果表明，在各种条件下，包括源高度相关的情况下，所提出的算法优于现有技术。还呈现了使用来自听觉实验的真实 EEG 数据的结果，以说明这些算法的性能。
