Anwendung trilinearer Komponentenanalyse auf die Analyse ereignisbezogener EEG-Potentiale

三线性分量分析在事件相关脑电电位分析中的应用

• 批准号: 144917516
• 负责人: Professor Dr. Rolf Verleger (†)
• 金额: --
• 依托单位: Klinik für Neurologie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2009
• 资助国家: 德国
• 起止时间: 2008-12-31 至 2011-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/144917516?language=en
• 关键词: Anwendung; trilinearer; Komponentenanalyse; auf; die

As recorded at the scalp, event-related EEG potentials (ERPs) consist of several overlapping components. To estimate parameters of these components, Principal Component Analysis (PCA) was recommended as the method of choice 25 years ago. However, PCA cannot arrive at a unique decomposition of the data without imposing additional constraints (nor can PCA s younger sibling , ICA). These constraints are usually formal (not model-driven), e.g., that some component should be focused on as few variables as possible (Varimax and Promax rotations), which does not necessarily lead to a decomposition that makes sense in physiological terms. Möcks (1988a, 1988b, 1991) suggested the model that a component should be characterized by its topographical distribution across recording sites being identical across observations. He proved that this leads to unique model-driven solutions and he developed an algorithm that reaches convergence. This Trilinear Components Analysis will here be applied to ERP analysis systematically for the first time. To reach this goal, the mathematical core of the present algorithm has to be tested, and alternative algorithmic options have to be realized. The critical test for application will be realized in an already available data set that has large experimental variance where we will look for an answer on the question how much the P3 component evoked by 82 In an 81-82 task overlaps with the downswinging overshoot ofthe preceding CNV.

根据头皮的记录，事件相关脑电电位(ERPs)由几个重叠的成分组成。为了估计这些成分的参数，主成分分析(PCA)在25年前被推荐为选择的方法。然而，主成分分析无法在不施加附加约束的情况下得到数据的唯一分解(主成分分析S弟弟也不能)。这些约束通常是正式的(不是模型驱动的)，例如，某个组件应该关注尽可能少的变量(Varimax和Promax旋转)，这不一定会导致在生理意义上有意义的分解。Möcks(1988a，1988b，1991)提出了一个模型，即一个组成部分的特征应该是其在记录地点的地形分布在不同观测中是相同的。他证明了这导致了独特的模型驱动的解决方案，并开发了一种达到收敛的算法。本文首次将这种三线性成分分析方法系统地应用于企业资源计划分析。为了实现这一目标，必须测试当前算法的数学核心，并必须实现替代算法选项。应用的关键测试将在一个已有的数据集中实现，该数据集具有很大的实验方差，在该数据集中，我们将寻找一个答案，即在81-82任务中由82诱发的P3分量在多大程度上与前一个CNV的下摆超调重叠。

项目成果

Parafac and go/no-go: disentangling CNV return from the P3 complex by trilinear component analysis.

• DOI: 10.1016/j.ijpsycho.2012.08.003
• 发表时间: 2013-03
• 期刊: International journal of psychophysiology : official journal of the International Organization of Psychophysiology
• 作者: R. Verleger;Claudia Paulick;J. Möcks;Janette L. Smith;Karsten Keller