Long Fei, Wu Xiaopei, Fan Ling
Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, China of Anhui University, Hefei 230039.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2003 Sep;20(3):479-83.
As a new array processing technique, independent component analysis(ICA) is an effective means to resolve the blind source separation(BSS) problem. Based on the brief introductions of ICA theory and algorithm, we apply ICA to the removal of ocular artifacts from EEG recordings. The EEG data collected from the human scalp is actually the mixtures of some independent components. It is coincident with the basic assumptions of ICA. Compared with the traditional methods of artifacts elimination, ICA, a kind of spatial filter, is not restricted by the case of spectrum overlapping, and it has a good reservation of useful detail signals. In addition, the inverse weight matrix of ICA can be used to reflect the topographic structure of different independent sources of EEG.
作为一种新的阵列处理技术，独立成分分析（ICA）是解决盲源分离（BSS）问题的有效手段。在简要介绍ICA理论和算法的基础上，我们将ICA应用于从脑电图记录中去除眼电伪迹。从人体头皮采集的脑电图数据实际上是一些独立成分的混合。这与ICA的基本假设相符。与传统的伪迹消除方法相比，ICA作为一种空间滤波器，不受频谱重叠情况的限制，并且能很好地保留有用的细节信号。此外，ICA的逆权重矩阵可用于反映脑电图不同独立源的地形结构。
