Briselli Ennio, Garreffa Girolamo, Bianchi Luigi, Bianciardi Marta, Macaluso Emiliano, Abbafati Manuel, Grazia Marciani Maria, Maraviglia Bruno
Museo storico della fisica e Centro studi e ricerche Enrico Fermi, Rome, Italy.
Magn Reson Imaging. 2006 May;24(4):393-400. doi: 10.1016/j.mri.2006.01.008. Epub 2006 Mar 20.
Interest about simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data acquisition has rapidly increased during the last years because of the possibility that the combined method offers to join temporal and spatial resolution, providing in this way a powerful tool to investigate spontaneous and evoked brain activities. However, several intrinsic features of MRI scanning become sources of artifacts on EEG data. Noise sources of a highly predictable nature such as those related to the pulse MRI sequence and those determined by magnetic gradient switching during scanning do not represent a major problem and can be easily removed. On the contrary, the ballistocardiogram (BCG) artifact, a large signal visible on all EEG traces and related to cardiac activity inside the magnetic field, is determined by sources that are not fully stereotyped and causing important limitations in the use of artifact-removing strategies. Recently, it has been proposed to use independent component analysis (ICA) to remove BCG artifact from EEG signals. ICA is a statistical algorithm that allows blind separation of statistically independent sources when the only available information is represented by their linear combination. An important drawback with most ICA algorithms is that they exhibit a stochastic behavior: each run yields slightly different results such that the reliability of the estimated sources is difficult to assess. In this preliminary report, we present a method based on running the FastICA algorithm many times with slightly different initial conditions. Clustering structure in the signal space of the obtained components provides us with a new way to assess the reliability of the estimated sources.
在过去几年中，由于同步脑电图（EEG）和功能磁共振成像（fMRI）数据采集的联合方法能够结合时间和空间分辨率，从而为研究自发和诱发脑活动提供了一个强大工具，因此人们对其的兴趣迅速增加。然而，MRI扫描的几个固有特征成为EEG数据中伪迹的来源。具有高度可预测性质的噪声源，如与脉冲MRI序列相关的噪声源以及扫描期间由磁梯度切换确定的噪声源，并不是主要问题，并且可以很容易地去除。相反，心冲击图（BCG）伪迹是在所有EEG迹线上都可见的大信号，与磁场内的心脏活动有关，它是由不完全定型的源决定的，这在使用伪迹去除策略时造成了重要限制。最近，有人提议使用独立成分分析（ICA）从EEG信号中去除BCG伪迹。ICA是一种统计算法，当唯一可用信息由其线性组合表示时，它允许对统计独立源进行盲分离。大多数ICA算法的一个重要缺点是它们表现出随机行为：每次运行都会产生略有不同的结果，因此难以评估估计源的可靠性。在本初步报告中，我们提出了一种基于多次以略有不同的初始条件运行FastICA算法的方法。在获得的成分的信号空间中的聚类结构为我们提供了一种评估估计源可靠性的新方法。
