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

Reza Mahini; Yansong Li; Weiyan Ding; Rao Fu; Tapani Ristaniemi; Asoke K Nandi; Guoliang Chen; Fengyu Cong

doi:10.3389/fnins.2020.521595

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

Front Neurosci. 2020 Oct 21:14:521595. doi: 10.3389/fnins.2020.521595. eCollection 2020.

Authors

Reza Mahini^{1

2}, Yansong Li^{3

4}, Weiyan Ding⁵, Rao Fu¹, Tapani Ristaniemi², Asoke K Nandi⁶, Guoliang Chen⁵, Fengyu Cong^{1

2

7

8}

Affiliations

¹ School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.
² Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, Finland.
³ Reward, Competition and Social Neuroscience Lab, Department of Psychology, School of Social and Behavioral Sciences, Nanjing University, Nanjing, China.
⁴ Institute for Brain Sciences, Nanjing University, Nanjing, China.
⁵ Department of Psychiatry, Chinese PLA 967th Hospital, Dalian, China.
⁶ Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, United Kingdom.
⁷ School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.
⁸ Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province, Dalian University of Technology, Dalian, China.

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; event-related potentials; microstates analysis; multi-set consensus clustering; time window.