A1 Journal article (refereed)
Determination of the Time Window of Event-Related Potential Using Multiple-Set Consensus Clustering (2020)


Mahini, Reza; Li, Yansong; Ding, Weiyan; Fu, Rao; Ristaniemi, Tapani; Nandi, Asoke K.; Chen, Guoliang; Cong, Fengyu (2020). Determination of the Time Window of Event-Related Potential Using Multiple-Set Consensus Clustering. Frontiers in Neuroscience, 14, 521595. DOI: 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

Volume: 14

Article number: 521595

Publisher: Frontiers Media SA

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.3389/fnins.2020.521595

Open Access: Publication published in an 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

Free keywords: multi-set consensus clustering; time window; event-related potentials; microstates analysis; cognitive neuroscience


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Preliminary JUFO rating: 1


Last updated on 2020-26-10 at 14:14