G5 Doctoral dissertation (article)
Consensus clustering for group-level analysis of event-related potential data (2023)
Konsensusklusterointi tapahtumakohtaisten potentiaalien ryhmätason analyysiin

Mahini Sheikhhosseini, R. (2023). Consensus clustering for group-level analysis of event-related potential data [Doctoral dissertation]. University of Jyväskylä. JYU dissertations, 733. https://urn.fi/URN:ISBN:978-951-39-9863-9

JYU authors or editors

Publication details

All authors or editors: Mahini Sheikhhosseini, Reza

eISBN: 978-951-39-9863-9

Journal or series: JYU dissertations

eISSN: 2489-9003

Publication year: 2023

Number in series: 733

Number of pages in the book: 1 verkkoaineisto (71 sivua, 64 sivua useina numerointijaksoina, 23 numeroimatonta sivua)

Publisher: University of Jyväskylä

Publication country: Finland

Publication language: English

Persistent website address: https://urn.fi/URN:ISBN:978-951-39-9863-9

Publication open access: Openly available

Publication channel open access: Open Access channel

Abstract

Understanding human brain activity through spatiotemporal electroencephalogram (EEG) analysis has gained prominence, with cluster analysis emerging as a valuable tool. While traditional event-related potential (ERP) analysis techniques for identifying interesting ERPs involve subjective time window selection, conventional cluster analysis focusing on spatial dynamics amplifies the risk of component identification errors when data is imperfect. Consequently, they do not offer a unified, appropriate time window determination approach for testing experimental hypotheses. This thesis introduces a series of consensus clustering-based approaches for examining brain responses in spatiotemporal ERP/EEG data. Specifically, the first study proposed a data-driven approach for determining the optimal number of clusters by evaluating the inner similarity of the estimated time window. A consensus clustering method from diverse clustering methods was also designed, including an M-N plot method for configuration. The second study proposed a multi-set consensus clustering approach across individual subjects to determine an appropriate (i.e., precise and stable) time window of ERP of interest. The time window determination method we developed examined two criteria for selecting a representative cluster map: inner similarity and hypothetical temporal coverage. The third study presented a multi-set consensus clustering approach for clustering analysis of single-trial EEG epochs that aimed to identify individual subjects’ evoked responses (ERP components). This study also introduced a standardized approach for evaluating scores from signal processing methods. Lastly, the fourth study introduced an ensemble deep clustering pipeline for reliably determining the time window when data quality is imperfect, revealing the adeptness of deep neural networks in feature extraction and time window determination. In conclusion, this thesis offers a promising computational framework for ERP identification in group-level analysis. The aforementioned studies enhance our understanding of human brain function, have broad implications for computational neuroscience, and suggest adaptable solutions for future neuroimaging investigations.

Keywords: computational neuroscience; clusters; joining together; cluster analysis; EEG; cognitive processes; doctoral dissertations

Free keywords: event-related potentials (ERPs); ensemble learning; consensus clustering; time windows; deep clustering; cluster aggregation

Contributing organizations

Ministry reporting: Yes

VIRTA submission year: 2023