A toolbox for residue iteration decomposition (RIDE)—A method for the decomposition, reconstruction, and single trial analysis of event related potentials
A toolbox for residue iteration decomposition (RIDE)—A method for the decomposition, reconstruction, and single trial analysis of event related potentials
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残差迭代分解 (RIDE) 工具箱——事件相关电位的分解、重构和单次试验分析方法
DOI：
10.1016/j.jneumeth.2014.10.009
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发表时间：
2015-07
期刊：
影响因子：
--
通讯作者：
Changsong Zhou
中科院分区：
文献类型：
--
作者：
Guang Ouyang;Werner Sommer;Changsong Zhou
背景传统的事件相关脑电位（event-related brain potentials，ERPs）是通过对多个单次试验进行平均来获得的。这可能是有问题的，因为试验到试验的延迟变化。残差迭代分解（RIDE）是一种将ERP分解为具有不同延迟变化的成分簇并将分离的成分重新同步到重构的ERP中的方法。新方法RIDE不断升级，现在收敛到一个健壮的版本。介绍了RIDE的原理和工具箱各功能模块的详细算法。我们给出建议，并提供使用RIDE从方法和心理学perspectives.ResultsRIDE适用于几个数据样本，以证明其分解和重建潜伏期变量的ERP组件和检索单次试验的变异性信息的能力。RIDE的不同功能在适当的例子中显示。与现有的方法相比，RIDE采用几个模块来实现ERP的鲁棒分解。RIDE的主要创新点是：（1）能够根据已知事件标记和估计时间的组合来提取成分;（2）比以前基于最小二乘算法的方法更有效地防止失真;（3）允许时间窗限制以与感兴趣的子过程相关的相关成分为目标。产生关于子成分的丰富信息，并且重建ERP，更紧密地反映单个试验ERP的组合活性。RIDE的结果为基于EEG数据研究脑-行为关系提供了新的维度。
BackgroundConventionally, event-related brain potentials (ERPs) are obtained by averaging a number of single trials. This can be problematic due to trial-to-trial latency variability. Residue iteration decomposition (RIDE) was developed to decompose ERPs into component clusters with different latency variability and to re-synchronize the separated components into a reconstructed ERP.New methodRIDE has been continuously upgraded and now converges to a robust version. We describe the principles of RIDE and detailed algorithms of the functional modules of a toolbox. We give recommendations and provide caveats for using RIDE from both methodological and psychological perspectives.ResultsRIDE was applied to several data samples to demonstrate its ability to decompose and reconstruct latency-variable components of ERPs and to retrieve single trial variability information. Different functionalities of RIDE were shown in appropriate examples.Comparison with existing methodsRIDE employs several modules to achieve a robust decomposition of ERP. As main innovations RIDE (1) is able to extract components based on the combination of known event markers and estimated latencies, (2) prevents distortions much more effectively than previous methods based on least-square algorithms, and (3) allows time window confinements to target relevant components associated with sub-processes of interest.ConclusionsRIDE is a convenient method that decomposes ERPs and provides single trial analysis, yielding rich information about sub-components, and that reconstructs ERPs, more closely reflecting the combined activity of single trial ERPs. The outcomes of RIDE provide new dimensions to study brain–behavior relationships based on EEG data.
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