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脑电逆问题的一种分域迭代聚焦算法
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脑电逆问题是一个欠定问题，引入适当的约束或迭代技巧是改善解的性能的有效途径。该文提出了一种将自相干增强算法(SCEA)和迭代聚焦算法(FOCUSS)相结合的新算法。该方法以最小模解为出发点，对其用聚类算法进行分区和自相干增强，然后进行迭代聚焦。针对皮层源的仿真计算表明，该方法可实现对不同强度的多源成像。
Electroencephalogram (EEG) inverse is an underdetermined problem. Various constraints or iteration strategies are being considered to improve the performance of the inverse. In this work, a new approach integrated Self-coherence Enhancement Algorithm (SECA) and Focal Underdetermined System Solver (FOCUSS) approach is proposed. Based on a minimum norm solution, this approach applies SECA to the multiple sub-regions of the solution space obtained, and then FOCUSS is utilized to further focalize the solution. The simulations for cortex sources confirm that this new approach may be applied to map a few un-equal strength cortical electric
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