Obregon-Henao G, Babadi B, Lamus C, Brown E N, Purdon P L
Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:6748-51. doi: 10.1109/EMBC.2012.6347543.
Recent dynamic source localization algorithms for the Magnetoencephalographic inverse problem use cortical spatio-temporal dynamics to enhance the quality of the estimation. However, these methods suffer from high computational complexity due to the large number of sources that must be estimated. In this work, we introduce a fast iterative greedy algorithm incorporating the class of subspace pursuit algorithms for sparse source localization. The algorithm employs a reduced order state-space model resulting in significant computational savings. Simulation studies on MEG source localization reveal substantial gains provided by the proposed method over the widely used minimum-norm estimate, in terms of localization accuracy, with a negligible increase in computational complexity.
最近用于脑磁图逆问题的动态源定位算法利用皮质时空动力学来提高估计质量。然而，由于必须估计的源数量众多，这些方法存在计算复杂度高的问题。在这项工作中，我们引入了一种快速迭代贪心算法，该算法结合了用于稀疏源定位的子空间追踪算法类。该算法采用降阶状态空间模型，从而显著节省计算量。对脑磁图源定位的模拟研究表明，与广泛使用的最小范数估计相比，所提出的方法在定位精度方面有显著提高，而计算复杂度的增加可忽略不计。
