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克服 ERP 方法的局限性:残差迭代分解(RIDE)在 Go/No-Go 实验中的应用。

Overcoming limitations of the ERP method with Residue Iteration Decomposition (RIDE): a demonstration in go/no-go experiments.

机构信息

Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong.

出版信息

Psychophysiology. 2013 Mar;50(3):253-65. doi: 10.1111/psyp.12004. Epub 2013 Jan 14.

Abstract

The usefulness of the event-related potential (ERP) method can be compromised by violations of the underlying assumptions, for example, confounding variations of latency and amplitude of ERP components within and between conditions. Here we show how the ERP subtraction method might yield misleading information due to latency variability of ERP components. We propose a solution to this problem by correcting for latency variability using Residue Iteration Decomposition (RIDE), demonstrated with data from representative go/no-go experiments. The overlap of N2 and P3 components in go/no-go data gives rise to spurious topographical localization of the no-go-N2 component. RIDE decomposes N2 and P3 based on their latency variability. The decomposition restored the N2 topography by removing the contamination from latency-variable late components. The RIDE-derived N2 and P3 give a clearer insight about their functional relevance in the go/no-go paradigm.

摘要

事件相关电位 (ERP) 方法的有效性可能会受到违反基本假设的影响,例如,在条件内和条件之间,ERP 成分的潜伏期和幅度的混杂变化。在这里,我们展示了由于 ERP 成分的潜伏期变化,ERP 相减方法可能会产生误导信息。我们通过使用残余迭代分解 (RIDE) 来纠正潜伏期变化,从而解决了这个问题,并通过具有代表性的 Go/No-Go 实验的数据来说明。Go/No-Go 数据中 N2 和 P3 成分的重叠导致了错误的无反应-N2 成分的拓扑定位。RIDE 根据潜伏期的变化对 N2 和 P3 进行分解。通过去除潜伏期变化的晚期成分的污染,分解恢复了 N2 的拓扑结构。RIDE 得出的 N2 和 P3 更清楚地揭示了它们在 Go/No-Go 范式中的功能相关性。

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