Department of Biomedical Engineering and Computational Science, Aalto University, Aalto, PO Box 12200, 00076 Espoo, Finland.
Med Biol Eng Comput. 2011 Apr;49(4):397-407. doi: 10.1007/s11517-011-0748-9. Epub 2011 Feb 18.
We present two techniques utilizing independent component analysis (ICA) to remove large muscle artifacts from transcranial magnetic stimulation (TMS)-evoked EEG signals. The first one is a novel semi-automatic technique, called enhanced deflation method (EDM). EDM is a modification of the deflation mode of the FastICA algorithm; with an enhanced independent component search, EDM is an effective tool for removing the large, spiky muscle artifacts. The second technique, called manual method (MaM) makes use of the symmetric mode of FastICA and the artifactual components are visually selected by the user. In order to evaluate the success of the artifact removal methods, four different quality parameters, based on curve comparison and frequency analysis, were studied. The dorsal premotor cortex (dPMC) and Broca's area (BA) were stimulated with TMS. Both methods removed the very large muscle artifacts recorded after stimulation of these brain areas. However, EDM was more stable, less subjective, and thus also faster to use than MaM. Until now, examining lateral areas of the cortex with TMS-EEG has been restricted because of strong muscle artifacts. The methods described here can remove those muscle artifacts, allowing one to study lateral areas of the human brain, e.g., BA, with TMS-EEG.
我们提出了两种利用独立成分分析（ICA）技术从经颅磁刺激（TMS）诱发的脑电图信号中去除大肌肉伪影的技术。第一种是一种新颖的半自动技术，称为增强排空法（EDM）。EDM 是 FastICA 算法排空模式的改进；通过增强独立成分搜索，EDM 是去除大而尖的肌肉伪影的有效工具。第二种技术称为手动方法（MaM），它利用了 FastICA 的对称模式，并且通过用户的视觉选择伪影成分。为了评估去伪影方法的成功，我们研究了基于曲线比较和频率分析的四个不同的质量参数。利用 TMS 刺激背侧运动前皮质（dPMC）和布罗卡区（BA）。两种方法都去除了在刺激这些脑区后记录到的非常大的肌肉伪影。然而，EDM 比 MaM 更稳定、更客观、使用起来也更快。到目前为止，由于强烈的肌肉伪影，用 TMS-EEG 检查皮质的外侧区域一直受到限制。这里描述的方法可以去除这些肌肉伪影，从而可以用 TMS-EEG 研究人类大脑的外侧区域，例如 BA。
