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

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