脑电图中磁/电偶极子的贝叶斯平滑
Bayesian smoothing of dipoles in Magneto-/Electro-encephalography
AI总结：
本文提出一种基于粒子滤波的动态估计多偶极子状态的新方法，通过蒙特卡洛算法近似平滑分布，提升源定位准确性，尤其在癫痫患者源建模中具有重要意义。
摘要：
我们描述了一种新颖的方法，用于从磁/电脑电图（M/EEG）时间序列中动态估计多偶极子状态。该新方法基于最近针对M/EEG开发的粒子滤波算法；这些算法通过样本和权重近似后验分布，即在时间t给定数据到时间t的神经源分布。然而，对于离线推断目的，更倾向于使用平滑分布，即神经源在时间t条件下整个时间序列的分布。在本研究中，我们使用蒙特卡洛算法近似时间变化的电流偶极子的平滑分布。通过数值模拟，我们展示出平滑分布提供的估计比滤波分布更准确，特别是在源出现时。我们使用记录自癫痫患者的实验数据集验证了所提算法。改进的源定位可能对癫痫患者源建模特别相关，因为源出现提供了关于癫痫灶的信息。
英文摘要：
We describe a novel method for dynamic estimation of multi-dipole states from Magneto/Electro-encephalography (M/EEG) time series. The new approach builds on the recent development of particle filters for M/EEG; these algorithms approximate, with samples and weights, the posterior distribution of the neural sources at time t given the data up to time t. However, for off-line inference purposes it is preferable to work with the smoothing distribution, i.e. the distribution for the neural sources at time t conditioned on the whole time series. In this study, we use a Monte Carlo algorithm to approximate the smoothing distribution for a time-varying set of current dipoles. We show, using numerical simulations, that the estimates provided by the smoothing distribution are more accurate than those provided by the filtering distribution, particularly at the appearance of the source. We validate the proposed algorithm using an experimental dataset recorded from an epileptic patient. Improved localization of the source onset can be particularly relevant in source modeling of epileptic patients, where the source onset brings information on the epileptogenic zone.
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