Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong.
Psychophysiology. 2011 Dec;48(12):1631-47. doi: 10.1111/j.1469-8986.2011.01269.x. Epub 2011 Sep 6.
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.
事件相关脑电位（ERPs）是重要的研究工具，因为它们可以提供有关高时间分辨率的心理处理的见解。然而，它们的有用性受到需要对大量试验进行平均的限制，从而牺牲了有关特定 ERP 成分潜伏期或幅度的试验间可变性的信息。在这里，我们提出了一种基于平均 ERP 残差的迭代策略（RIDE）的新方法，以分离潜伏期可变的成分簇。然后，分离的成分簇可以用作模板，以高精度估计单个试验的潜伏期。通过将 RIDE 应用于面孔启动实验的数据，我们分离了启动效应，并表明它们对潜伏期变化和条件内可变性具有鲁棒性。RIDE 对于表现出不同程度的变异性和 ERP 成分之间的时间重叠的各种数据集都很有用。
