James Christopher J, Demanuele Charmaine
Institute of Digital Healthcare, WMG, University of Warwick, Coventry, CV4 7AL, UK.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:1898-901. doi: 10.1109/IEMBS.2010.5627351.
Independent Component Analysis (ICA) is a very common instantiation of the Blind Source Separation (BSS) problem. In the context of biomedical signal analysis, ICA is generally applied to multi-channel recordings of physiological phenomena in order to de-noise and extract meaningful information underlying the recordings. This paper assesses the Spatio-Temporal ICA (ST-ICA) framework, which uses both spatial and temporal information derived from multi-channel time-series to extract underlying sources. In contrast, the standard implementation of the ICA algorithm generally uses only limited spatial information to inform the separation process. One of the major steps in the implementation of any ICA algorithm is the selection of relevant components from the many ICA usually returns. With ST-ICA there is a rich data-set of components exhibiting spatial as well as temporal/spectral information that could be used to identify the underlying process subspaces extracted by the ST-ICA algorithm. This paper highlights the methodology for performing ST-ICA and assesses the possible ways in which process subspace identification may take place.
独立成分分析（ICA）是盲源分离（BSS）问题的一种非常常见的实例。在生物医学信号分析的背景下，ICA通常应用于生理现象的多通道记录，以便去噪并提取记录背后的有意义信息。本文评估了时空独立成分分析（ST-ICA）框架，该框架利用从多通道时间序列中导出的空间和时间信息来提取潜在源。相比之下，ICA算法的标准实现通常仅使用有限的空间信息来指导分离过程。任何ICA算法实现中的一个主要步骤是从ICA通常返回的众多成分中选择相关成分。对于ST-ICA，有一组丰富的成分数据集，这些成分展示了空间以及时间/频谱信息，可用于识别由ST-ICA算法提取的潜在过程子空间。本文重点介绍了执行ST-ICA的方法，并评估了进行过程子空间识别的可能方式。
