This work was partially supported by the HKBU Strategic Development Fund and the Hong Kong Research Grants Council (HKBU 202710) to G.O. and C.Z., a grant by the German Academic Exchange Service (DAAD) to G.O., and a grant by the German Research Foundation to W.S. (So177/17-1). This research was conducted using the resources of the High Performance Cluster Computing Centre, Hong Kong Baptist University, which receives funding from Research Grant Council, University Grant Committee of the HKSAR and Hong Kong Baptist University.
Overcoming limitations of the ERP method with Residue Iteration Decomposition (RIDE): A demonstration in go/no-go experiments
Article first published online: 14 JAN 2013
Copyright © 2013 Society for Psychophysiological Research
Volume 50, Issue 3, pages 253–265, March 2013
How to Cite
Ouyang, G., Schacht, A., Zhou, C. and Sommer, W. (2013), Overcoming limitations of the ERP method with Residue Iteration Decomposition (RIDE): A demonstration in go/no-go experiments. Psychophysiology, 50: 253–265. doi: 10.1111/psyp.12004
- Issue published online: 5 FEB 2013
- Article first published online: 14 JAN 2013
- the HKBU Strategic Development Fund
- Hong Kong Research Grants Council. Grant Number: HKBU 202710
- German Academic Exchange Service (DAAD)
- German Research Foundation. Grant Number: So177/17-1
- ERP subtraction/difference waves;
- Go/no-go paradigm;
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.