James Christopher J, Demanuele Charmaine
Signal Processing and Control Group, ISVR, University of Southampton, SO17 1BJ, UK.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:3154-7. doi: 10.1109/IEMBS.2009.5334034.
This paper assesses the use of Independent Component Analysis (ICA) as applied to epileptic scalp electroencephalographic (EEG) recordings. In particular we address the newly introduced Spatio-Temporal ICA algorithm (ST-ICA), which uses both spatial and temporal information derived from multi-channel biomedical signal recordings to inform (or update) the standard ICA algorithm. ICA is a technique well suited to extracting underlying sources from multi-channel EEG recordings - for ictal EEG recordings, the goal is to both de-noise the EEG recordings (i.e. remove artifacts) as well as isolate and extract epileptic processes. As part of any ICA application, there is an interim stage whereby relevant components (or processes) need to be identified - either objectively or subjectively (usually the latter). In previous work with ST-ICA we used spectral information alone to identify the underlying processes subspaces extracted by the ST-ICA. Here we assess the joint use of spatial as well as spectral information for this purpose. We test this on ictal EEG segments where it can be seen that different underlying processes possess characteristic signatures in both modalities which can be utilized for the clustering (or process selection) stage.
本文评估了独立成分分析（ICA）应用于癫痫头皮脑电图（EEG）记录的情况。特别地，我们探讨了新引入的时空独立成分分析算法（ST-ICA），该算法利用从多通道生物医学信号记录中获取的空间和时间信息来为标准ICA算法提供信息（或进行更新）。ICA是一种非常适合从多通道EEG记录中提取潜在源的技术——对于发作期EEG记录，目标是对EEG记录进行去噪（即去除伪迹）以及分离和提取癫痫过程。作为任何ICA应用的一部分，都有一个中间阶段，在此阶段需要客观或主观地识别相关成分（或过程）（通常是后者）。在之前使用ST-ICA的工作中，我们仅使用频谱信息来识别由ST-ICA提取的潜在过程子空间。在此，我们评估为此目的联合使用空间和频谱信息的情况。我们在发作期EEG片段上对此进行测试，从中可以看出，不同的潜在过程在这两种模式下都具有特征性特征，可用于聚类（或过程选择）阶段。
