Residue iteration decomposition (RIDE): A new method to separate ERP components on the basis of latency variability in single trials

Abstract

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.

Event-related lateralized readiness potential correlates of the emotion-priming Simon effect

“…Previous researches indicated that there might be an overlap between conventional LRP and N2cc component when the stimuli were presented in the Simon task with horizontal S-R arrangements (Praamstra and Oostenveld 2003;Praamstra 2006;Cespón et al 2012). The RIDE, employed in the present study, was a useful tool for separating the different ERP components (Ouyang et al 2011(Ouyang et al , 2015bStürmer et al 2013;Verleger et al 2014;Wang et al 2015), and the RIDE-separated LRP components were devoid of distortions inherent to standard LRPs (Stürmer et al 2013). In the RIDE-reconstructed data, a significant N2cc effect of Congruence was found in the C component cluster.…”

“…However, recent studies have stated that using techniques for disentangling N2cc/LRP overlap in the Simon task with horizontal S-R arrangements might be more attractive because it allowed a better understanding of the cognitive control process that involved in the Simon effect (Leuthold 2011). RIDE was such a method that has been shown to sharpen the distinction between different ERP components (Ouyang et al 2011(Ouyang et al , 2015bStürmer et al 2013;Verleger et al 2014;Wang et al 2015). Stürmer et al (2013) indicated that RIDE was a useful tool for separating LRP component, and the RIDE-separated LRP components were devoid of distortions inherent to standard LRPs.…”

A toolbox for residue iteration decomposition (RIDE)—A method for the decomposition, reconstruction, and single trial analysis of event related potentials

“…RIDE was initially proposed by Ouyang et al (2011) to separate ERP into components with or without time markers based on an iteration procedure; however, in some datasets the initial version of RIDE produced inconsistent results, which are rooted in the distortion problems due to L2-norm minimization employed in the first version of RIDE. After continuous upgrading, RIDE algorithms have now become robust, showing much more reliable performance, especially in avoiding the distortions inherent in the least-square scheme.…”

“…The estimated latency of the components that are supposed to be marker-locked (e.g., stimulus-locked) was shown to be too variable due to the effects of noise. Ouyang et al (2011) suggested a new method, termed Residue Iteration Decomposition (RIDE), aiming to solve some limitations of stimulus-locked average ERPs. RIDE combined the information of available time markers and estimated latencies to extract both marker-locked and non-marker-locked component clusters.…”

Re-examination of Chinese Semantic Processing and Syntactic Processing: Evidence from Conventional ERPs and Reconstructed ERPs by Residue Iteration Decomposition (RIDE)

“…Another novel aspect of the current study is that a new method, Residue Iteration Decomposition (RIDE) [33][34][35][36], was applied to the present data in addition to the conventional ERP analysis. The motivation to use RIDE is that several previous studies using the conventional ERP analysis reported similar effects in the N400 time window when comparing SEM with SEM+SYN, which might be the result of the smearing effect caused by trial-to-trial variability; that is, the trial-to-trial latency variability of ERP components could diminish the ERP amplitude after averaging.…”