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

独立分量分析（伊卡）是一种信号处理技术，多年来一直被用作从身体上进行的许多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，伊卡提供了一种非常有用的方法，可以从脑电图和脑磁图（分别为EEG和MEG）的记录中提取有关神经源的信息。伊卡是关于从一组混合测量中分离统计独立的源-例如从EEG中提取眨眼等。伊卡的标准方法通常与提取类似数量的独立源的多个通道一起工作。在以前的工作中，它已被证明，“单通道”伊卡是可能的，本身是一个非常强大的技术，用于提取多个源的基础上一个单一的通道测量。而“标准”伊卡可以被称为“空间”伊卡，单通道伊卡可以被称为“时间”伊卡-因为由于单通道布置，没有空间信息通知伊卡过程。伊卡的逻辑进展是执行时空伊卡，其中伊卡过程是通过从一组神经信号记录中导出的空间和时间/频谱信息来通知的。可以证明，时空伊卡产生了一种强大的算法，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，标准伊卡也有同样的问题，包括线性、无噪声、统计独立的源混合假设，以及伊卡完成后选择相关源的困境。对于空时伊卡，由于维数灾难，问题变得复杂。该项目将建立在以前的工作，加强伊卡过程和应用技术，以各种EEG信号数据库。该项目的另一部分将涉及建立和运行各种EEG数据收集练习，将伊卡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.