Determination of the Time Window of Event-Related Potential Using Multiple-Set Consensus Clustering

• Main Authors: Reza Mahini, Yansong Li, Weiyan Ding, Rao Fu, Tapani Ristaniemi, Asoke K. Nandi, Guoliang Chen, Fengyu Cong
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-10-01
• Series: Frontiers in Neuroscience
• Subjects: multi-set consensus clustering; time window; event-related potentials; microstates analysis; cognitive neuroscience
• Online Access: https://www.frontiersin.org/articles/10.3389/fnins.2020.521595/full

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.