Clustering is a promising tool for grouping the sequence of similar time-points aimed to identify the attention blocks in spatiotemporal event-related potentials (ERPs) analysis. It is most likely to elicit the appropriate time window for ERP of interest if a suitable clustering method is applied to spatiotemporal ERP. However, how to reliably estimate a proper time window from entire individual subjects\' data is still challenging. In this study, we developed a novel multiset consensus clustering method in which several clustering results of multiple subjects were combined to retrieve the best fitted clustering for all the subjects within a group. Then, the obtained clustering was processed by a newly proposed time-window detection method to determine the most suitable time window for identifying the ERP of interest in each condition/group. Applying the proposed method to the simulated ERP data and real data indicated that the brain responses from the individual subjects can be collected to determine a reliable time window for different conditions/groups. Our results revealed more precise time windows to identify N2 and P3 components in the simulated data compared to the state-of-the-art methods. Additionally, our proposed method achieved more robust performance and outperformed statistical analysis results in the real data for N300 and prospective positivity components. To conclude, the proposed method successfully estimates the time window for ERP of interest by processing the individual data, offering new venues for spatiotemporal ERP processing.

Failing to consider psychometric issues related to reliability and validity, differential deficits, and statistical power potentially undermines the conclusions of a study. In research using event-related brain potentials (ERPs), numerous contextual factors (population sampled, task, data recording, analysis pipeline, etc.) can impact the reliability of ERP scores. The present review considers the contextual factors that influence ERP score reliability and the downstream effects that reliability has on statistical analyses. Given the context-dependent nature of ERPs, it is recommended that ERP score reliability be formally assessed on a study-by-study basis. Recommended guidelines for ERP studies include 1) reporting the threshold of acceptable reliability and reliability estimates for observed scores, 2) specifying the approach used to estimate reliability, and 3) justifying how trial-count minima were chosen. A reliability threshold for internal consistency of at least 0.70 is recommended, and a threshold of 0.80 is preferred. The review also advocates the use of generalizability theory for estimating score dependability (the generalizability theory analog to reliability) as an improvement on classical test theory reliability estimates, suggesting that the latter is less well suited to ERP research. To facilitate the calculation and reporting of dependability estimates, an open-source Matlab program, the ERP Reliability Analysis Toolbox, is presented.

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