Wu Shun Chi, Swindlehurst A Lee
Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:6572-5. doi: 10.1109/IEMBS.2011.6091621.
A matching pursuit (MP) based algorithm, called source deflated matching pursuit (SDMP), is proposed for locating sources of brain activity. By iteratively deflating the contribution of identified sources to multiple measurement vectors (MMVs), the SDMP algorithm transforms the original multi-basis-vector/matrix selection problem into a single-basis-vector/matrix selection problem, which not only mitigates the residual-source interference but also remedies the intrinsic bias when locating deep sources. The robustness of the proposed algorithm to two bias factors is verified through simulations.
提出了一种基于匹配追踪（MP）的算法，称为源消减匹配追踪（SDMP），用于定位大脑活动源。通过迭代消减已识别源对多个测量向量（MMV）的贡献，SDMP算法将原始的多基向量/矩阵选择问题转化为单基向量/矩阵选择问题，这不仅减轻了残留源干扰，还纠正了定位深部源时的固有偏差。通过仿真验证了所提算法对两个偏差因素的鲁棒性。
