Pontifex Matthew B, Gwizdala Kathryn L, Parks Andrew C, Billinger Martin, Brunner Clemens
Department of Kinesiology, Michigan State University, East Lansing, Michigan, USA.
Department of Otolaryngology, Hannover Medical School, Hanover, Germany.
Psychophysiology. 2017 Mar;54(3):386-398. doi: 10.1111/psyp.12804. Epub 2016 Dec 27.
Despite the growing use of independent component analysis (ICA) algorithms for isolating and removing eyeblink-related activity from EEG data, we have limited understanding of how variability associated with ICA uncertainty may be influencing the reconstructed EEG signal after removing the eyeblink artifact components. To characterize the magnitude of this ICA uncertainty and to understand the extent to which it may influence findings within ERP and EEG investigations, ICA decompositions of EEG data from 32 college-aged young adults were repeated 30 times for three popular ICA algorithms. Following each decomposition, eyeblink components were identified and removed. The remaining components were back-projected, and the resulting clean EEG data were further used to analyze ERPs. Findings revealed that ICA uncertainty results in variation in P3 amplitude as well as variation across all EEG sampling points, but differs across ICA algorithms as a function of the spatial location of the EEG channel. This investigation highlights the potential of ICA uncertainty to introduce additional sources of variance when the data are back-projected without artifact components. Careful selection of ICA algorithms and parameters can reduce the extent to which ICA uncertainty may introduce an additional source of variance within ERP/EEG studies.
尽管独立成分分析（ICA）算法在从脑电图（EEG）数据中分离和去除与眨眼相关的活动方面的应用越来越广泛，但我们对去除眨眼伪迹成分后，与ICA不确定性相关的变异性如何影响重建的EEG信号了解有限。为了表征这种ICA不确定性的程度，并了解其在事件相关电位（ERP）和EEG研究中可能影响研究结果的程度，对来自32名大学生年龄的年轻人的EEG数据，针对三种常用的ICA算法重复进行了30次ICA分解。每次分解后，识别并去除眨眼成分。将剩余成分进行反向投影，并将得到的干净EEG数据进一步用于分析ERP。研究结果表明，ICA不确定性导致P3波幅变化以及所有EEG采样点的变化，但不同ICA算法的这种变化因EEG通道的空间位置而异。这项研究突出了在没有伪迹成分的情况下对数据进行反向投影时，ICA不确定性引入额外方差来源的可能性。仔细选择ICA算法和参数可以减少ICA不确定性在ERP/EEG研究中引入额外方差来源的程度。
