School of Psychology, University of Birmingham, Birmingham, UK.
Neuroimage. 2010 Mar;50(1):112-23. doi: 10.1016/j.neuroimage.2009.12.002. Epub 2009 Dec 16.
EEG quality is a crucial issue when acquiring combined EEG-fMRI data, particularly when the focus is on using single trial (ST) variability to integrate the data sets. The most common method for improving EEG data quality following removal of gross MRI artefacts is independent component analysis (ICA), a completely blind source separation technique. In the current study, a different approach is proposed based on the functional source separation (FSS) algorithm. FSS is an extension of ICA that incorporates prior knowledge about the signal of interest into the data decomposition. Since in general the part of the EEG signal that will contain the most relevant information is known beforehand (i.e. evoked potential peaks, spectral bands), FSS separates the signal of interest by exploiting this prior knowledge without renouncing the advantages of using only information contained in the original signal waveforms. A reversing checkerboard stimulus was used to generate visual evoked potentials (VEPs) in healthy control subjects. Gradient and ballistocardiogram artefacts were removed with template subtraction techniques to form the raw data, which were then subjected to ICA denoising and FSS. The resulting EEG data sets were compared using several metrics derived from average and ST data and correlated with fMRI data. In all cases, ICA was an improvement on the raw data, but the most obvious improvement was provided by FSS, which consistently outperformed ICA. The results show the benefit of FSS for the recovery of good quality single trial evoked potentials during concurrent EEG-fMRI recordings.
当获取组合 EEG-fMRI 数据时，EEG 质量是一个关键问题，特别是当重点是使用单试（ST）变异性来整合数据集时。去除大体 MRI 伪影后提高 EEG 数据质量最常用的方法是独立成分分析（ICA），这是一种完全盲源分离技术。在当前的研究中，提出了一种基于功能源分离（FSS）算法的不同方法。FSS 是 ICA 的扩展，它将关于感兴趣信号的先验知识纳入数据分解中。由于通常情况下，包含最相关信息的 EEG 信号部分是事先已知的（即诱发电位峰值、频谱带），因此 FSS 通过利用这种先验知识来分离感兴趣的信号，而不会放弃仅使用原始信号波形中包含的信息的优势。使用反转棋盘刺激在健康对照受试者中产生视觉诱发电位（VEPs）。使用模板减法技术去除梯度和心动图伪影，形成原始数据，然后对原始数据进行 ICA 去噪和 FSS。使用从平均和 ST 数据导出的几个指标比较所得 EEG 数据集，并与 fMRI 数据相关联。在所有情况下，ICA 都优于原始数据，但 FSS 提供了最明显的改进，始终优于 ICA。结果表明，在同时进行 EEG-fMRI 记录时，FSS 有利于恢复高质量的单试诱发电位。
