Brain & Behaviour Research Institute and School of Psychology, University of Wollongong, Wollongong, NSW 2522, Australia.
Brain & Behaviour Research Institute and School of Psychology, University of Wollongong, Wollongong, NSW 2522, Australia.
J Neurosci Methods. 2019 Jun 1;321:1-11. doi: 10.1016/j.jneumeth.2019.04.001. Epub 2019 Apr 4.
The majority of electroencephalographic (EEG) investigations in normal ageing have determined EEG spectra from epochs recorded in the eyes-closed (EC) and/or eyes-open (EO) resting states, and summed amplitudes or power estimates within somewhat-arbitrary and/or inconsistently defined traditional frequency band limits.
Natural frequency components were sought using a data-driven frequency Principal Components Analysis (f-PCA) approach, optimised to reduce between-condition and between-group misallocation of variance. Frequency component correspondence was screened using the Congruence Coefficient and topographic correlations for potential matches on Condition and/or Group. The amplitudes of corresponding natural components were then explored as a function of these independent variables.
Separate f-PCAs with Young and Older adults' EC and EO data each yielded between six and nine components that peaked across the traditional delta to beta band ranges. Across these, two components were matched on Group and Condition, while a further six were matched on Condition (within-groups), and four on Group (within-conditions).
Multiple frequency components were found within the traditional bands, and provided a wider perspective in terms of additional natural component details. In addition to novel insights, the well-documented age-related spectral reductions were seen in the common delta component, and in one EC (but no EO) alpha component.
This combination of optimised f-PCA approach and component screening procedure has wide potential in the EEG field beyond the ageing topic explored here, being applicable across an extensive range of studies using EEG oscillations to explore aspects of cognitive processing and individual differences.
大多数正常老化的脑电图（EEG）研究都是通过记录闭眼（EC）和/或睁眼（EO）静息状态的时间段，并在某些任意和/或不一致定义的传统频段范围内对幅度或功率估计进行求和来确定 EEG 谱。
使用数据驱动的频率主成分分析（f-PCA）方法寻找自然频率分量，该方法经过优化可减少条件和组之间的方差分配错误。使用一致性系数和地形图相关性筛选频率分量对应关系，以寻找与条件和/或组的潜在匹配。然后，探索相应自然分量的幅度作为这些独立变量的函数。
分别对年轻和老年成年人的 EC 和 EO 数据进行 f-PCA，每个 PCA 都产生了 6 到 9 个在传统 delta 到 beta 波段范围内达到峰值的分量。在这些分量中，有两个在组和条件上匹配，有六个在条件（组内）上匹配，四个在组（组内）上匹配。
在传统频段内发现了多个频率分量，为附加自然分量细节提供了更广泛的视角。除了新的见解外，还在常见的 delta 分量和一个 EC（但不是 EO）alpha 分量中观察到与年龄相关的频谱减少。
这种优化的 f-PCA 方法和分量筛选程序的组合在 EEG 领域具有广泛的应用潜力，除了这里探讨的老化主题外，还适用于使用 EEG 振荡来探索认知处理和个体差异方面的广泛研究。
