Optimal Number of Clusters by Measuring Similarity Among Topographies for Spatio-Temporal ERP Analysis
Publiceringsår
2022
Upphovspersoner
Mahini, Reza; Xu, Peng; Chen, Guoliang; Li, Yansong; Ding, Weiyan; Zhang, Lei; Qureshi, Nauman Khalid; Hämäläinen, Timo; Nandi, Asoke K.; Cong, Fengyu
Abstrakt
Averaging amplitudes over consecutive time samples (i.e., time window) is widely used to calculate the peak amplitude of event-related potentials (ERPs). Cluster analysis of the spatio-temporal ERP data is a promising tool to determine the time window of an ERP of interest. However, determining an appropriate number of clusters to optimally represent ERPs is still challenging. Here, we develop a new method to estimate the optimal number of clusters utilizing consensus clustering. Various polarity dependent clustering methods, namely, k-means, hierarchical clustering, fuzzy c-means, self-organizing map, spectral clustering, and Gaussian mixture model, are used to configure consensus clustering after assessing them individually. When a range of clusters is applied many times, the optimal number of clusters should correspond to the expectation, which is the average of the obtained mean inner-similarities of estimated time windows across all conditions and groups converge in the satisfactory thresholds. In order to assess our method, the proposed method has been applied to simulated data and prospective memory experiment ERP data aimed to qualify N2 and P3, and N300 and prospective positivity components, respectively. The results of determining the optimal number of clusters meet at six cluster maps for both ERP data. In addition, our results revealed that the proposed method could be reliably applied to ERP data to determine the appropriate time window for the ERP of interest when the measurement interval is not accurately defined.
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Publikationstyp
Publikationsform
Artikel
Moderpublikationens typ
Tidning
Artikelstyp
En originalartikel
Målgrupp
VetenskapligKollegialt utvärderad
Kollegialt utvärderadUKM:s publikationstyp
A1 Originalartikel i en vetenskaplig tidskriftPublikationskanalens uppgifter
Journal
Förläggare
Volym
35
Nummer
5-6
Sidor
537-557
ISSN
Publikationsforum
Publikationsforumsnivå
1
Öppen tillgång
Öppen tillgänglighet i förläggarens tjänst
Nej
Parallellsparad
Nej
Övriga uppgifter
Vetenskapsområden
Data- och informationsvetenskap; Neurovetenskaper
Nyckelord
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Publiceringsland
Förenta staterna (USA)
Förlagets internationalitet
Internationell
Språk
engelska
Internationell sampublikation
Ja
Sampublikation med ett företag
Nej
DOI
10.1007/s10548-022-00903-2
Publikationen ingår i undervisnings- och kulturministeriets datainsamling
Ja