An independent component ballistocardiogram analysis-based approach on artifact removing
An independent component ballistocardiogram analysis-based approach on artifact removing
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DOI：
10.1016/j.mri.2006.01.008
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发表时间：
2006-05-01
影响因子：
2.5
通讯作者：
Maraviglia, Bruno
中科院分区：
文献类型：
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作者：
Briselli, Ennio;Garreffa, Girolamo;Maraviglia, Bruno
在过去的几年里，关于同时脑电图（EEG）和功能磁共振成像（fMRI）数据采集的兴趣迅速增加，因为组合的方法提供了加入时间和空间分辨率的可能性，以这种方式提供了一个强大的工具来研究自发和诱发的大脑活动。然而，MRI扫描的一些固有特征成为EEG数据上的伪影的来源。具有高度可预测性质的噪声源，例如与脉冲MRI序列相关的噪声源和扫描期间由磁梯度切换确定的噪声源，并不构成主要问题，并且可以容易地去除。相反，心冲击图（BCG）伪影，一个大的信号上可见的所有EEG轨迹和相关的磁场内的心脏活动，是由不完全定型的来源，并在使用伪影去除策略造成重要的限制。伊卡是一种统计算法，当唯一可用的信息由它们的线性组合表示时，它允许对统计独立的源进行盲分离。大多数伊卡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。在这份初步报告中，我们提出了一种基于运行FastICA算法多次略有不同的初始条件的方法。所得到的分量在信号空间中的聚类结构为我们提供了一种新的方法来评估估计源的可靠性。(c)2006年爱思唯尔公司All rights reserved.
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. (c) 2006 Elsevier Inc. All rights reserved.