用于源解析ERP分析的非参数组水平统计
Non-parametric group-level statistics for source-resolved ERP analysis.
作者信息
Lee Clement, Miyakoshi Makoto, Delorme Arnaud, Cauwenberghs Gert, Makeig Scott
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7450-3. doi: 10.1109/EMBC.2015.7320114.
We have developed a new statistical framework for group-level event-related potential (ERP) analysis in EEGLAB. The framework calculates the variance of scalp channel signals accounted for by the activity of homogeneous clusters of sources found by independent component analysis (ICA). When ICA data decomposition is performed on each subject's data separately, functionally equivalent ICs can be grouped into EEGLAB clusters. Here, we report a new addition (statPvaf) to the EEGLAB plug-in std_envtopo to enable inferential statistics on main effects and interactions in event related potentials (ERPs) of independent component (IC) processes at the group level. We demonstrate the use of the updated plug-in on simulated and actual EEG data.
我们已经为EEGLAB中基于组水平的事件相关电位（ERP）分析开发了一个新的统计框架。该框架计算由独立成分分析（ICA）找到的源的同质簇的活动所解释的头皮通道信号的方差。当对每个受试者的数据分别进行ICA数据分解时，功能等效的独立成分（IC）可以被分组到EEGLAB簇中。在这里，我们报告了EEGLAB插件std_envtopo的一个新补充（statPvaf），以实现对组水平上独立成分（IC）过程的事件相关电位（ERP）中的主效应和交互作用进行推断统计。我们展示了更新后的插件在模拟和实际脑电图数据上的使用。
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