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)是一种多年来流行的信号处理技术，用于从人体内进行的大量生物医学信号测量中提取有意义的信息。特别是在神经工程领域，ICA提供了一种非常有用的方法，可以从脑电和脑磁图(分别为脑电和脑磁图)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出统计上独立的源，例如从脑电中提取眨眼等。在神经工程案例中，通常很好地满足潜在源统计独立的强烈假设。ICA的标准方法通常与多个通道一起工作，这些通道提取类似数量的独立信号源。在以前的工作中已经表明，单通道ICA是可能的，并且本身是一种非常强大的技术，用于提取作为单通道测量基础的多个源。“标准”ICA可以被称为“空间”ICA，而单通道ICA可以被称为“时间”ICA--因为由于单通道的排列，没有空间信息来通知ICA过程。ICA的逻辑进程是执行时空ICA，从而通过从一组神经信号记录获得的空间和时间/光谱信息来通知ICA过程。可以证明，时空ICA产生了一种强大的算法，可以在多种条件下从大脑信号记录中提取有意义的信息。这项技术并非没有问题，它面临着与标准ICA相同的问题；包括围绕线性、无噪声、统计独立的信源混合假设的问题，以及在ICA完成后选择相关信源的两难境地。对于时空ICA，由于维度诅咒，这个问题变得复杂起来。该项目将在以前工作的基础上，加强ICA过程，并将这些技术应用于各种脑电信号数据库。该项目的另一部分将涉及建立和运行各种脑电数据收集练习，将独立成分分析技术应用于脑-计算机接口范例。

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.