To recover the neural activity from Magnetoencephalography (MEG) and Electroencephalography (EEG) measurements, we need to solve the inverse problem by utilizing the relation between dipole sources and the data generated by dipolar sources. In this study, we propose a new approach based on the implementation of a particle filter (PF) that uses minimum sampling variance resampling methodology to track the neural dipole sources of cerebral activity. We use this approach for the EEG data and demonstrate that it can naturally estimate the sources more precisely than the traditional systematic resampling scheme in PFs.

中文翻译：

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使用具有最佳比例集重采样的粒子滤波器进行神经源定位

为了从磁脑电图（MEG）和脑电图（EEG）测量中恢复神经活动，我们需要利用偶极子源与偶极子源生成的数据之间的关系来解决反问题。在这项研究中，我们提出了一种基于粒子滤波器（PF）实现的新方法，该滤波器使用最小采样方差重采样方法来跟踪大脑活动的神经偶极子源。我们将这种方法用于EEG数据，并证明与传统的PFs系统重采样方案相比，它可以自然地更准确地估计来源。