Bayesian Denoising and Source Localization for MEG Data

描述(由申请人提供)：精确定位参与各种任务的神经生成器的三维位置的能力对于许多神经生理学研究至关重要，例如获得对人类认知的多种过程的正确理解，以及对癫痫灶定位的术前辅助。该提案的主要目的是将新的统计信号处理工具应用于原始脑磁图数据，以便产生更准确的定位。该方案包括两种方法，第一种方法是利用神经信号之间的统计关系，以减少原始脑磁图数据中固有的噪声和干扰。在该方法中，使用标准算法之一来定位所得到的去噪信号。第二种方法涉及将定位和去噪算法结合到单个功能单元中。虽然最近使用独立分量分析(ICA)进行去噪变得流行，但这种方法有几个缺点，例如，模型阶数的选择以及确定哪些分量对应于感兴趣的信号以及哪些分量是干扰。提出的方法使用了一种新的贝叶斯推理公式，该公式不受这些缺陷的阻碍。

DESCRIPTION (provided by applicant): The ability to pinpoint the three-dimensional location of the neural generators involved in various tasks is paramount to many neurophysiological studies, such as gaining a correct understanding of the manifold processes of human cognition, and for preoperative assistance in epileptic foci localization. The broad aim of the proposal is to apply novel statistical signal processing tools to raw MEG data in order to produce more accurate localizations. The proposed scheme consists of two approaches, the first of which concerns taking advantage of the statistical relationships among neural signals in order to reduce the inherent noise and interference in raw MEG data. In this approach the resulting denoised signals are localized using one of the standard algorithms. The second approach involves combining the localization and the denoising algorithm into a single functional unit. While it has recently become popular to use Independent Component Analysis (ICA) for denoising, there are several drawbacks to this approach, e.g., model order selection and the determination of which components correspond to signals of interest and which are interference. The proposed method uses a novel Bayesian inference formulation that is not hindered by these deficiencies.