Wang X F, Yang Qi, Fan Zhaozhi, Sun Chang-Kai, Yue Guang H
Department of Quantitative Health Sciences, Cleveland Clinic, 9500 Euclid Avenue/JJN3, Cleveland, OH 44195, USA.
J Neurosci Methods. 2009 Feb 15;177(1):232-40. doi: 10.1016/j.jneumeth.2008.09.030. Epub 2008 Oct 10.
This study investigates time-dependent associations between source strength estimated from high-density scalp electroencephalogram (EEG) and force of voluntary handgrip contraction at different intensity levels. We first estimate source strength from raw EEG signals collected during voluntary muscle contractions at different levels and then propose a functional random-effects model approach in which both functional fixed effects and functional random-effects are considered for the data. Two estimation procedures for the functional model are discussed. The first estimation procedure is a two-step method which involves no iterations. It can flexibly use different smoothing methods and smoothing parameters. The second estimation procedure benefits from the connection between linear mixed models and regression splines and can be fitted using existing software. Functional ANOVA is then suggested to assess the experimental effects from the functional point of view. The statistical analysis shows that the time-dependent source strength function exhibits a nonlinear feature, where a bump is detected around the force onset time. However, there is the lack of significant variations in source strength on different force levels and different cortical areas. The proposed functional random-effects model procedure can be applied to other types of functional data in neuroscience.
本研究调查了从高密度头皮脑电图（EEG）估计的源强度与不同强度水平下自愿性手握力收缩力之间的时间依赖性关联。我们首先从不同水平的自愿性肌肉收缩期间收集的原始EEG信号中估计源强度，然后提出一种功能随机效应模型方法，其中对数据同时考虑功能固定效应和功能随机效应。讨论了功能模型的两种估计程序。第一种估计程序是一种两步法，无需迭代。它可以灵活使用不同的平滑方法和平滑参数。第二种估计程序受益于线性混合模型与回归样条之间的联系，并且可以使用现有软件进行拟合。然后建议使用功能方差分析从功能角度评估实验效果。统计分析表明，时间依赖性源强度函数呈现非线性特征，在力开始时间附近检测到一个峰值。然而，在不同力水平和不同皮质区域，源强度缺乏显著变化。所提出的功能随机效应模型程序可应用于神经科学中其他类型的功能数据。
