Department of Biomedical Engineering and Computational Science (BECS), Aalto University, School of Science, P.O. Box 12200, FI-00076 Aalto, Espoo, Finland.
J Neurosci Methods. 2012 Jul 30;209(1):144-57. doi: 10.1016/j.jneumeth.2012.05.029. Epub 2012 Jun 9.
Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a powerful tool for studying cortical excitability and connectivity. To enhance the EEG interpretation, independent component analysis (ICA) has been used to separate the data into independent components (ICs). However, TMS can evoke large artifacts in EEG, which may greatly distort the ICA separation. The removal of such artifactual EEG from the data is a difficult task. In this paper we study how badly the large artifacts distort the ICA separation, and whether the distortions could be avoided without removing the artifacts. We first show that, in the ICA separation, the time courses of the ICs are not affected by the large artifacts, but their topographies could be greatly distorted. Next, we show how this distortion can be circumvented. We introduce a novel technique of suppression, by which the EEG data are modified so that the ICA separation of the suppressed data becomes reliable. The suppression, instead of removing the artifactual EEG, rescales all the data to about the same magnitude as the neural EEG. For the suppressed data, ICA returns the original time courses, but instead of the original topographies, it returns modified ones, which can be used, e.g., for the source localization. We present three suppression methods based on principal component analysis, wavelet analysis, and whitening of the data matrix, respectively. We test the methods with numerical simulations. The results show that the suppression improves the source localization.
经颅磁刺激（TMS）与脑电图（EEG）相结合是研究皮质兴奋性和连通性的有力工具。为了增强 EEG 解释，已使用独立成分分析（ICA）将数据分离成独立成分（IC）。然而，TMS 会在 EEG 中引起较大的伪影，这可能会极大地扭曲 ICA 分离。从数据中去除这种人为的 EEG 是一项艰巨的任务。在本文中，我们研究了大伪影如何严重扭曲 ICA 分离，以及是否可以在不去除伪影的情况下避免这种扭曲。我们首先表明，在 ICA 分离中，IC 的时程不受大伪影的影响，但它们的地形图可能会被严重扭曲。接下来，我们展示了如何避免这种扭曲。我们引入了一种新的抑制技术，通过该技术对 EEG 数据进行修改，以便对抑制数据进行可靠的 ICA 分离。这种抑制不是去除人为的 EEG，而是将所有数据缩放到与神经 EEG 大致相同的幅度。对于抑制的数据，ICA 返回原始时程，但返回的是修改后的地形图，例如，可以用于源定位。我们分别提出了基于主成分分析、小波分析和数据矩阵白化的三种抑制方法。我们使用数值模拟测试了这些方法。结果表明，抑制可以改善源定位。
