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: 1117056
• 负责人: Tulay Adali
• 金额: $ 33.42万
• 依托单位: University of Maryland Baltimore County
• 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 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117056&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）在许多学科（包括信号处理）中发现了广泛使用，因为它是从简单的生成模型开始最小化数据生成机制的假设，并实现了观察到的数据的有用分解。特别是，独立的组件分析（ICA）是实现BSS的最常用方法，因为在许多应用中，基本组件的统计独立性是合理的。除独立性外，样本相关是许多感兴趣的信号的另一个固有属性。传统上，这两种属性在开发用于源分离的方法时将分别解决。另一方面，熵率是一种自然成本，可以共同考虑独立性和样本相关性，因此有望导致一类新的具有广泛适用性的强大解决方案。此外，它使一个人能够轻松地合并模型选择 - 通过使用信息理论标准，将BSS的功能补充的另一个关键问题将补充BSS的力量补充到问题中。这项研究的重点是开发一类强大的方法，用于使用熵率的源分离和模型选择，以便将同时提高信息和样品相关的问题与更高的绩效相关，以实现较高的绩效问题。主要的应用领域是可以真正利用这种完全合并的方法的一个：功能磁共振（fMRI）数据的分析以及在EEG-EEG-FMRI数据中脑电图（EEG）中梯度和脉冲伪影的排斥。两者都是由于这些问题的噪声和伪影的独特性质而被证明对传统模型方法具有挑战性的应用。因此，它们为本研究开发的新方法提供了独特的测试床。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，因此新的方法将是许多其他问题的有吸引力的解决方案。