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.