标题： 贝叶斯混合效应模型用于多级双向函数数据：脑电图实验的应用 显示英文标题

标题： Bayesian Mixed-Effects Models for Multilevel Two-way Functional Data: Applications to EEG Experiments

摘要： 在多条件脑电图实验中，当受试者执行各种任务或暴露于不同刺激时，记录大脑活动。 记录的信号通常被转换为时频表示，这些表示在时间和频率维度上通常表现出平滑的变化。 这些表示自然地结构为两向函数数据，其中实验条件嵌套在受试者内。 现有的分析方法无法同时考虑数据的多级结构、函数性质以及对受试者水平协变量的依赖性。 为解决这些限制，我们提出了一种适用于两向函数数据的贝叶斯混合效应模型，该模型在条件水平上包含与协变量相关的固定效应，并具有多级随机效应。 为了增强模型的可解释性和简洁性，我们引入了一种新的与协变量相关的CANDECOMP/PARAFAC（CP）分解方法用于固定效应，该方法具有边缘可解释的时间和频率模式。 我们进一步提出了一种促进稀疏性的先验用于CP秩选择，并设计了一种高效的后验抽样算法。 所提出的方法通过大量模拟进行了评估，并应用于收集的脑电图数据，以研究酗酒对视觉刺激下认知加工的影响。 我们的分析揭示了与酗酒相关的时频活动的不同模式，为不同受试者群体和实验条件之间的神经加工差异提供了新的见解。 显示英文摘要

摘要： In multi-condition EEG experiments, brain activity is recorded as subjects perform various tasks or are exposed to different stimuli. The recorded signals are commonly transformed into time-frequency representations, which often display smooth variations across time and frequency dimensions. These representations are naturally structured as two-way functional data, with experimental conditions nested within subjects. Existing analytical methods fail to jointly account for the data's multilevel structure, functional nature, and dependence on subject-level covariates. To address these limitations, we propose a Bayesian mixed-effects model for two-way functional data that incorporates covariate-dependent fixed effects at the condition level and multilevel random effects. For enhanced model interpretability and parsimony, we introduce a novel covariate-dependent CANDECOMP/PARAFAC (CP) decomposition for the fixed effects, with marginally interpretable time and frequency patterns. We further propose a sparsity-inducing prior for CP rank selection and an efficient algorithm for posterior sampling. The proposed method is evaluated through extensive simulations and applied to EEG data collected to investigate the effects of alcoholism on cognitive processing in response to visual stimuli. Our analysis reveals distinct patterns of time-frequency activity associated with alcoholism, offering new insights into the neural processing differences between subject groups and experimental conditions.