A1 Journal article (refereed)
Determination of the Time Window of Event-Related Potential Using Multiple-Set Consensus Clustering (2020)
Mahini, R., Li, Y., Ding, W., Fu, R., Ristaniemi, T., Nandi, A. K., Chen, G., & Cong, F. (2020). Determination of the Time Window of Event-Related Potential Using Multiple-Set Consensus Clustering. Frontiers in Neuroscience, 14, Article 521595. https://doi.org/10.3389/fnins.2020.521595
JYU authors or editors
Publication details
All authors or editors: Mahini, Reza; Li, Yansong; Ding, Weiyan; Fu, Rao; Ristaniemi, Tapani; Nandi, Asoke K.; Chen, Guoliang; Cong, Fengyu
Journal or series: Frontiers in Neuroscience
ISSN: 1662-4548
eISSN: 1662-453X
Publication year: 2020
Publication date: 21/10/2020
Volume: 14
Article number: 521595
Publisher: Frontiers Media SA
Publication country: Switzerland
Publication language: English
DOI: https://doi.org/10.3389/fnins.2020.521595
Publication open access: Openly available
Publication channel open access: Open Access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/72335
Abstract
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.
Keywords: cognitive neuroscience; signal analysis; signal processing; cluster analysis
Contributing organizations
Ministry reporting: Yes
Reporting Year: 2020
JUFO rating: 1
