Updating and validating a new framework for restoring and analyzing latency-variable ERP components from single trials with residue iteration decomposition (RIDE)

Trial-to-trial latency variability pervades cognitive EEG responses and may mix and smear ERP components but is usually ignored in conventional ERP averaging. Existing attempts to decompose temporally overlapping and latency-variable ERP components show major limitations. Here, we propose a theoretical framework and model of ERPs consisting of temporally overlapping components locked to different external events or varying in latency from trial to trial. Based on this model, a new ERP decomposition and reconstruction method was developed: residue iteration decomposition (RIDE). Here, we describe an update of the method and compare it to other decomposition methods in simulated and real datasets. The updated RIDE method solves the divergence problem inherent to previous latency-based decomposition methods. By implementing the model of ERPs as consisting of time-variable and invariable single-trial component clusters, RIDE obtains latency-corrected ERP waveforms and topographies of the components, and yields dynamic information about single trials.