Department of Psychiatry and Psychotherapy of the University Clinic of Leipzig, Leipzig, Germany.
Neuroimage. 2011 Jan 1;54(1):1-3; discussion 4-9. doi: 10.1016/j.neuroimage.2010.04.256. Epub 2010 May 2.
Independent component analysis (ICA)-based muscle artefact correction has become a popular tool within electroencephalographic (EEG) research. As a comment on the article by McMenamin et al. (2010), we want to address three issues concerning the claimed lack of sensitivity and specificity of this method. The under- or overestimation of myogenic and neurogenic signals after ICA-based muscle artefact correction reported by McMenamin et al. might be explainable in part by a) insufficient temporal independence of myogenic and neurogenic components when exploring more than one condition, b) wrong classification of myogenic or neurogenic components by human raters and c) differences of neuronal mass activity during tensed or relaxed-muscle conditions. Our own data show only significant differences regarding intracortical alpha band EEG-source estimates for contrasts between clean EEG data and artificially contaminated EEG data at group-analysis level but not between clean data and data after ICA-based correction. ICA-based artefact correction already provides a powerful tool for muscle artefact rejection. More research is needed for determining reliable criteria to delineate myogenic from neurogenic components.
基于独立成分分析(ICA)的肌电伪迹校正已成为脑电图(EEG)研究中的一种流行工具。作为对 McMenamin 等人(2010 年)文章的评论,我们想解决该方法被声称的灵敏度和特异性不足的三个问题。McMenamin 等人报告的 ICA 肌电伪迹校正后肌源性和神经源性信号的低估或高估,部分原因可能是 a)在探索一个以上条件时,肌源性和神经源性成分的时间独立性不足,b)人类评分者对肌源性或神经源性成分的错误分类,以及 c)在紧张或放松肌肉条件下神经元群体活动的差异。我们自己的数据仅显示在组分析水平上,对于干净的 EEG 数据与人为污染的 EEG 数据之间的对比的皮质内 alpha 波段 EEG 源估计存在显著差异,但在干净数据和 ICA 校正后的数据之间不存在差异。基于 ICA 的伪迹校正已经为肌电伪迹的去除提供了一个强大的工具。需要更多的研究来确定可靠的标准,以区分肌源性和神经源性成分。