• ERP averaging;
  • ERP signal-to-noise ratio;
  • High-resolution averages;
  • Reaction-time distributions;
  • Variable-latency ERPs;
  • Grand averages


Stimulus-locked, response-locked, and ERP-locked averaging are effective methods for reducing artifacts in ERP analysis. However, they suffer from a magnifying-glass effect: they increase the resolution of specific ERPs at the cost of blurring other ERPs. Here we propose an extremely simple technique—binning trials based on response times and then averaging—which can significantly alleviate the problems of other averaging methods. We have empirically evaluated the technique in an experiment where the task requires detecting a target in the presence of distractors. We have also studied the signal-to-noise ratio and the resolving power of averages with and without binning. Results indicate that the method produces clearer representations of ERPs than either stimulus-locked and response-locked averaging, revealing finer details of ERPs and helping in the evaluation of the amplitude and latency of ERP waves. The method is applicable to within-subject and between-subject averages.