Residue iteration decomposition (RIDE): A new method to separate ERP components on the basis of latency variability in single trials
Residue iteration decomposition (RIDE): A new method to separate ERP components on the basis of latency variability in single trials
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DOI：
10.1111/j.1469-8986.2011.01269.x
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发表时间：
2011-12-01
期刊：
影响因子：
3.7
通讯作者：
Sommer, Werner
中科院分区：
文献类型：
--
作者：
Guang Ouyang;Herzmann, Grit;Sommer, Werner
事件相关脑电位(ERPs)是一种重要的研究工具，因为它们提供了对高时间分辨率的心理加工的洞察。然而，由于需要对大量试验进行平均，牺牲了关于具体事件相关电位成分的潜伏期或波幅的逐个试验的可变性的信息，它们的用处受到了限制。这里，我们提出了一种基于平均事件相关电位剩余迭代策略(RIDE)的新方法来分离潜伏期可变的成分簇。然后，分离的组件簇可以用作模板，以高精度地估计单次试验中的潜伏期。通过将RIDE应用于人脸启动实验的数据，我们分离了启动效应，并表明它们对潜伏期变化和条件内变异性具有健壮性。RIDE对于显示ERP组件之间不同程度的可变性和时间重叠的各种数据集很有用。
Event-related brain potentials (ERPs) are important research tools because they provide insights into mental processing at high temporal resolution. Their usefulness, however, is limited by the need to average over a large number of trials, sacrificing information about the trial-by-trial variability of latencies or amplitudes of specific ERP components. Here we propose a novel method based on an iteration strategy of the residues of averaged ERPs (RIDE) to separate latency-variable component clusters. The separated component clusters can then serve as templates to estimate latencies in single trials with high precision. By applying RIDE to data from a face-priming experiment, we separate priming effects and show that they are robust against latency shifts and within-condition variability. RIDE is useful for a variety of data sets that show different degrees of variability and temporal overlap between ERP components.