Repeated Stochastic Processes in Single Trial EEG-Analysis - Statistical Theory and Methods

心理学家和行为经济学家对反复观察随机过程产生的数据越来越感兴趣。项目TP2， TP6旨在回答基于这类数据的双重处理（自动和受控过程）背景下的复杂问题。平均erp的标准方法通常需要大量的同质试验。这在处理微观经济决策时可能会出现问题，例如，在微观经济决策中，可用的重复次数通常很少。此外，试验平均忽略了实验试验中自动和受控过程的可变性。因此，单试验分析在本RU的上下文中是合适的方法。由于单次试验的数据结构较为复杂，可用的数据集相对较少，因此需要复杂而简洁的统计方法来提取基本信息。在这个项目中，该方法将在适当定义的随机过程的背景下，通过考虑功能数据分析（FDA）和独立成分分析（ICA）来发展。数据分析的重点将是脑电图数据（erp）及其在模拟人类决策中的预测能力（TP2, TP6）。统计方法将适用于FDA和ICA领域内的企业时间和/或空间依赖结构。这将提供更多的洞察决策过程及其神经相关

There is a growing interest among psychologists and behavioural economists in data generated by repeated observations of a stochastic process. Projects TP2, TP6 aim at answering complex questions in the context of dual processing (automatic and controlled processes) based on data of this type. The standard approach of averaging ERPs usually requires a large number of homogeneous trials. This can be problematic when dealing, for instance, with microeconomic decisions where the number of available repetitions is often small. Moreover, trial averaging ignores variability of automatic and controlled processes across experimental trials. Single-trial analysis is therefore the appropriate approach in the context of this RU. Since the data structure in single trials is more complex and available data sets are relatively small, sophisticated and parsimonious statistical methodology is needed to extract the essential information. In this project this methodology will be developed by considering functional data analysis (FDA) and independent component analysis (ICA) in the context of suitably defined stochastic processes. The data-analytical focus will be on EEG data (ERPs) and their predictive power in modelling human decision making (TP2, TP6). The statistical methods will in corporate temporal and/or spatial dependence structures within the realm of FDA and ICA. This will provide more insight into the process of decision making and its neural correlates