Particle Filters for Magnetoencephalography
Particle Filters for Magnetoencephalography
复制标题
用于脑磁图的粒子过滤器
DOI：
10.1007/s11831-010-9047-0
复制
发表时间：
2010
影响因子：
9.7
通讯作者：
A. Sorrentino
中科院分区：
文献类型：
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作者：
A. Sorrentino
脑磁图 (MEG) 是一种用于大脑功能研究的强大技术，可以在毫秒时间尺度上研究神经动力学。基于反问题的求解，从测量的磁场中定位神经源由于几个问题而变得复杂。首先，这个问题是不适定的：有无限多个电流分布同样可以很好地解释给定的测量结果。其次，数据上的噪声量非常高，而噪声的主要来源是大脑本身。第三，该问题是动态的，因为数据的时间分辨率与神经动力学的时间尺度具有相同的量级。在过去的二十年里，许多不同的方法被提出并应用来解决 MEG 反问题；然而，对 MEG 源建模的可靠且通用且自动的方法的搜索仍然处于开放状态。最近，我们致力于将一类新的算法（称为粒子滤波器）应用于 MEG 问题。在这里，我们尝试回顾这些方法，并展示在合成和实验 MEG 数据上获得的令人鼓舞的结果。
Magnetoencephalography (MEG) is a powerful technique for brain functional studies, which allows investigation of the neural dynamics on a millisecond time-scale. The localization of the neural sources from the measured magnetic fields, based on the solution of an inverse problem, is complicated by several issues. First, the problem is ill-posed: there are infinitely many current distributions explaining a given measurement equally well. Second, the amount of noise on the data is very high, and the main source of noise is the brain itself. Third, the problem is dynamical because the temporal resolution of the data is of the same order of the temporal scale of the neural dynamics. In the last two decades, many different methods have been proposed and applied for solving the MEG inverse problem; however, the search for a reliable yet general and automatic approach to MEG source modeling is still open. Recently we have worked at applying a new class of algorithms, known as particle filters, to the MEG problem. Here we attempt a review of these methods and show encouraging results obtained on both synthetic and experimental MEG data.
DOI：
10.1016/0013-4694(95)00107-a
发表时间：
1995-10-01
期刊：
ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY
影响因子：
--
作者：
GORODNITSKY, IF;GEORGE, JS;RAO, BD
通讯作者：
RAO, BD