Tenke Craig E, Kayser Jürgen
Department of Biopsychology, New York State Psychiatric Institute, NY 10032-2695, USA.
Clin Neurophysiol. 2005 Dec;116(12):2826-46. doi: 10.1016/j.clinph.2005.08.007. Epub 2005 Oct 28.
Definition of appropriate frequency bands and choice of recording reference limit the interpretability of quantitative EEG, which may be further compromised by distorted topographies or inverted hemispheric asymmetries when employing conventional (non-linear) power spectra. In contrast, fPCA factors conform to the spectral structure of empirical data, and a surface Laplacian (2-dimensional CSD) simplifies topographies by minimizing volume-conducted activity. Conciseness and interpretability of EEG and CSD fPCA solutions were compared for three common scaling methods.
Resting EEG and CSD (30 channels, nose reference, eyes open/closed) from 51 healthy and 93 clinically-depressed adults were simplified as power, log power, and amplitude spectra, and summarized using unrestricted, Varimax-rotated, covariance-based fPCA.
Multiple alpha factors were separable from artifact and reproducible across subgroups. Power spectra produced numerous, sharply-defined factors emphasizing low frequencies. Log power spectra produced fewer, broader factors emphasizing high frequencies. Solutions for amplitude spectra showed optimal intermediate tuning, particularly when derived from CSD rather than EEG spectra. These solutions were topographically distinct, detecting multiple posterior alpha generators but excluding the dorsal surface of the frontal lobes. Instead a low alpha/theta factor showed a secondary topography along the frontal midline.
CSD amplitude spectrum fPCA solutions provide simpler, reference-independent measures that more directly reflect neuronal activity.
A new quantitative EEG approach affording spectral components is developed that closely parallels the concept of an ERP component in the temporal domain.
合适频段的定义以及记录参考的选择限制了定量脑电图（EEG）的可解释性，当采用传统（非线性）功率谱时，地形扭曲或半球不对称反转可能会进一步损害其可解释性。相比之下，功能主成分分析（fPCA）因子符合经验数据的频谱结构，表面拉普拉斯算子（二维电流源密度，CSD）通过最小化容积传导活动简化了地形。比较了三种常见缩放方法下EEG和CSD的fPCA解决方案的简洁性和可解释性。
将51名健康成年人和93名临床抑郁症成年人的静息EEG和CSD（30个通道，鼻参考，睁眼/闭眼）简化为功率谱、对数功率谱和幅度谱，并使用无限制、方差最大化旋转、基于协方差的fPCA进行汇总。
多个α因子可与伪迹分离，且在各亚组间具有可重复性。功率谱产生了许多强调低频的清晰定义的因子。对数功率谱产生的因子较少，更强调高频。幅度谱的解决方案显示出最佳的中间调谐，特别是从CSD谱而非EEG谱得出时。这些解决方案在地形上是不同的，检测到多个后部α发生器，但不包括额叶背侧表面。相反，一个低α/θ因子在额中线沿线显示出二级地形。
CSD幅度谱fPCA解决方案提供了更简单、与参考无关的测量方法，能更直接地反映神经元活动。
开发了一种新的定量EEG方法，可提供频谱成分，与时域中ERP成分的概念密切平行。
