Electroencephalography (EEG) is a noninvasive neuroimaging modality that captures electrical brain activity many times per second. We seek to estimate power spectra from EEG data that ware gathered for 557 adolescent twin pairs through the Minnesota Twin Family Study (MTFS). Typically, spectral analysis methods treat time series from each subject separately, and independent spectral densities are fit to each time series. Since the EEG data were collected on twins, it is reasonable to assume that the time series have similar underlying characteristics, so borrowing information across subjects can significantly improve estimation. We propose a Nested Bernstein Dirichlet prior model to estimate the power spectrum of the EEG signal for each subject by smoothing periodograms within and across subjects while requiring minimal user input to tuning parameters. Furthermore, we leverage the MTFS twin study design to estimate the heritability of EEG power spectra with the hopes of establishing new endophenotypes. Through simulation studies designed to mimic the MTFS, we show our method out-performs a set of other popular methods.

中文翻译：

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用于估计静息态脑电图双胞胎数据谱密度的非参数贝叶斯模型

脑电图 (EEG) 是一种非侵入性神经成像方式，每秒可多次捕获脑电活动。我们试图从通过明尼苏达双胞胎家庭研究 (MTFS) 为 557 对青少年双胞胎收集的 EEG 数据估计功率谱。通常，光谱分析方法分别处理来自每个受试者的时间序列，并且独立的光谱密度适合每个时间序列。由于脑电图数据是在双胞胎上收集的，因此可以合理地假设时间序列具有相似的潜在特征，因此跨受试者借用信息可以显着改善估计。我们提出了一个嵌套 Bernstein Dirichlet 先验模型，通过平滑受试者内部和跨受试者的周期图来估计每个受试者的 EEG 信号的功率谱，同时需要最少的用户输入来调整参数。此外，我们利用 MTFS 双胞胎研究设计来估计 EEG 功率谱的遗传力，希望建立新的内表型。通过旨在模仿 MTFS 的模拟研究，我们展示了我们的方法优于一组其他流行的方法。