Collaborative Research: Complex-Valued Signal Processing and its Application to Analysis of Brain Imaging Data

合作研究：复值信号处理及其在脑成像数据分析中的应用复值信号经常出现在通信、雷达和生物医学等各种应用中，因为大多数实际调制格式都是复类型的，并且雷达和磁共振成像等应用导致数据具有固有的复值。复域不仅为这些信号提供了方便的表示，而且还提供了一种自然的方式来保留信号的物理特性和它们所经历的变换。然而，复域，也提出了一些挑战，在推导和分析的信号处理算法，并作为一个结果，绝大多数的算法开发的复域都采取了捷径限制其useful.This研究建立了一个框架，复值信号处理，使复值信号处理的全部潜力可以实现。它允许所有的计算都在复数域中进行，从而消除了在算法的推导和分析中对许多简化假设的需要，例如信号的圆度。它还允许使用完全复杂的函数，而不是更常用的有界但非解析函数。这些功能提供了有吸引力的替代品，通过有效地生成高阶统计信息进行独立分量分析（伊卡）。 使用这个框架，一类新的高效的算法推导出执行伊卡在复杂的域，特别是，用于研究大脑功能的医学成像数据在其原生的，复杂的形式。

Collaborative Research: Complex-Valued Signal Processing and its Application to Analysis of Brain Imaging DataComplex-valued signals arise frequently in applications as diverse as communications, radar, and biomedicine, as most practical modulation formats are of complex type and applications such as radar and magnetic resonance imaging lead to data that are inherently complex valued. The complex domain not only provides a convenient representation for these signals but also a natural way to preserve the physical characteristics of the signals and the transformations they go though. The complex domain, however, also presents a number of challenges in the derivation and analysis of signal processing algorithms, and as a result, the vast majority of algorithms developed for the complex domain have taken shortcuts limiting their usefulness.This research establishes a framework for complex-valued signal processing such that the full potential of complex-valued signal processing can be realized. It allows for all computations to be carried out in the complex domain eliminating the need for many simplifying assumptions, such as the circularity of signal, both in the derivation and the analysis of the algorithms. It also allows for the use of fully complex functions rather than the more commonly utilized bounded but non-analytic functions. These functions provide attractive alternatives for performing independent component analysis(ICA) by efficiently generating higher-order statistical information. Using this framework, a new class of efficient algorithms are derived for performing ICA in the complex domain, in particular, for studying brain function using the medical imaging data in its native, complex form.