CIF: Small: Collaborative Research: Entropy Rate for Source Separation and Model Selection: Applications in fMRI and EEG Analysis

CIF：小型：合作研究：源分离和模型选择的熵率：在功能磁共振成像和脑电图分析中的应用

• 批准号: 1116944
• 负责人: Vince Calhoun
• 金额: $ 15.92万
• 依托单位: The MIND Research Network
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116944&HistoricalAwards=false
• 关键词: CIF; Small; Collaborative; Research; Entropy

Blind source separation (BSS) has found wide use in many disciplines including signal processing as it starts from a simple generative model minimizing assumptions on the data generation mechanism and achieves useful decompositions of the observed data. In particular, independent component analysis (ICA) has been the most commonly used approach to achieve BSS since statistical independence of the underlying components is plausible in many applications. Besides independence, sample correlation is another inherent property of many signals of interest. Traditionally, these two properties are addressed separately when developing methods for source separation. Entropy rate, on the other hand, is a natural cost that allows one to account for independence and sample correlation jointly, and hence promises to result in a new class of powerful solutions with wide applicability. In addition, it enables one to easily incorporate model selection---another key problem complementing the power of BSS---into the problem through the use of information theoretic criteria.The focus of this research is the development of a class of powerful methods for source separation and model selection using entropy rate so that one can take both the higher-order-statistical information and sample correlation into account to achieve significant performance gains in more challenging problems. The main application domain is one that can truly take advantage of this fully combined approach: the analysis of functional magnetic resonance (fMRI) data and the rejection of gradient and pulse artifacts in electroencephalography (EEG) in concurrent EEG-fMRI data. Both are applications that have proven challenging for the traditional model-based approach due to the unique nature of the noise and artifacts in these problems. Hence, they provide a unique testbed for the performance evaluation of the new class of methods developed under this study. Since independence and sample correlation are intrinsic properties of many other types of data, the new set of methods will be attractive solutions for many other problems as well.

盲源分离（BSS）已在包括信号处理在内的许多学科中得到广泛应用，因为它从一个简单的生成模型开始，最大限度地减少对数据生成机制的假设，并实现对观测数据的有用分解。特别是，独立成分分析 (ICA) 一直是实现 BSS 最常用的方法，因为底层组件的统计独立性在许多应用中是合理的。除了独立性之外，样本相关性是许多感兴趣信号的另一个固有属性。传统上，在开发源分离方法时，会分别解决这两个属性。另一方面，熵率是一种自然成本，允许人们共同考虑独立性和样本相关性，因此有望产生一类具有广泛适用性的新型强大解决方案。此外，它还使人们能够通过使用信息论标准轻松地将模型选择（补充 BSS 功能的另一个关键问题）纳入到问题中。本研究的重点是开发一类强大的方法使用熵率进行源分离和模型选择，以便可以同时考虑高阶统计信息和样本相关性，以在更具挑战性的问题中实现显着的性能提升。主要应用领域是能够真正利用这种完全组合方法的领域：功能磁共振 (fMRI) 数据分析以及并发 EEG-fMRI 数据中脑电图 (EEG) 中梯度和脉冲伪影的剔除。由于这些问题中噪声和伪影的独特性质，这两种应用都已被证明对传统的基于模型的方法具有挑战性。因此，它们为本研究中开发的新型方法的性能评估提供了一个独特的测试平台。由于独立性和样本相关性是许多其他类型数据的固有属性，因此这组新方法也将成为许多其他问题的有吸引力的解决方案。