On the use of Independent Component Analysis for brain signal processing 1=Healthcare technologies 2=Digital Signal Processing

独立分量分析（ICA）是一种信号处理技术，近年来越来越受欢迎，它被用作从身体各处的生物医学信号测量中提取有意义信息的手段。特别是在神经工程中，ICA提供了一种非常有用的方法，可以从脑电图和脑磁图（EEG和MEG）的记录中提取有关神经源的信息。ICA是关于从一组混合测量中分离统计上独立的来源-例如从脑电图中提取眨眼等。在神经工程案例中，底层源的统计独立性的强假设通常得到很好的满足。ICA的标准方法通常适用于提取相似数量的独立源的多个通道。在以前的工作中，已经证明“单通道”ICA是可能的，并且它本身是一种非常强大的技术，用于提取单通道测量下的多个源。“标准”ICA可以被称为“空间”ICA，而单通道ICA可以被称为“时间”ICA，因为由于单通道安排，ICA过程没有空间信息。ICA的逻辑进展是执行时空ICA，其中ICA过程通过从一组神经信号记录中获得的空间和时间/频谱信息来通知。可以证明，时空ICA产生了一个强大的算法，可以在许多条件下从大脑信号记录中提取有意义的信息。该技术并非没有问题，它也存在与标准ICA相同的问题；包括围绕线性、无噪声、统计独立混合源的假设问题，以及ICA完成后选择相关源的困境。在时空独立分量分析中，由于维度的诅咒，问题变得更加复杂。本项目将在先前工作的基础上，加强ICA过程并将技术应用于各种脑电图信号数据库。该项目的另一个部分将涉及建立和运行各种脑电图数据收集练习，将ICA技术应用于脑机接口范例。

Independent Component Analysis (ICA) is a signal processing technique that has gained in popularity over the years used as a means of extracting meaningful information from a number of biomedical signal measurements made across the body. In Neural Engineering in particular, ICA has provided a very useful means of extracting information about neural sources from recordings of the electroencephalogram and magnetoencephalogram - (EEG and MEG respectively). ICA is about the separation of statistically independent sources from a set of mixed measurements - for example in extracting eye-blinks from EEG, etc. The strong assumption of statistical independence of the underlying sources is usually well met in neural engineering cases. Standard methods of ICA usually work with multiple channels that extract a similar number of independent sources. In previous work it has been shown that 'single-channel' ICA is possible and is in itself a very powerful technique for the extraction of multiple sources underlying a single channel measurement. Whereas 'standard' ICA can be termed as 'spatial' ICA, single channel ICA can be termed as 'temporal' ICA - as there is no spatial information informing the ICA process due to the single channel arrangement. The logical progression for ICA is to perform spatio-temporal ICA, whereby the ICA process is informed by means of both spatial and temporal/spectral information derived from a set of neural signal recordings. It can be shown that space-time ICA results in a powerful algorithm that can extract meaningful information in brain signal recordings across a number of conditions. The technique is not without its issues, suffering from the same problems standard ICA suffers from; including issues around the assumptions of linear, noiseless, statistically independent mixing of sources as well as the dilemma of choosing relevant sources after ICA is complete. With space-time ICA the problem is compounded due to the curse of dimensionality. This project will build on previous work, enhancing the ICA process and applying the techniques to various EEG signal databases. A further part of the project will involve the setting up and running of various EEG data gathering exercises applying the ICA techniques to brain-computer interfacing paradigms.