Validating rationale of group-level component analysis based on estimating number of sources in EEG through model order selection

Fengyu Cong, Zhaoshui He, Jarmo Hämäläinen, Paavo H.T. Leppänen, Heikki Lyytinen, Andrzej Cichocki, Tapani Ristaniemi

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)

Abstract

This study addresses how to validate the rationale of group component analysis (CA) for blind source separation through estimating the number of sources in each individual EEG dataset via model order selection. Control children, typically reading children with risk for reading disability (RD), and children with RD participated in the experiment. Passive oddball paradigm was used for eliciting mismatch negativity during EEG data collection. Data were cleaned by two digital filters with pass bands of 1-30. Hz and 1-15. Hz and a wavelet filter with the pass band narrower than 1-12. Hz. Three model order selection methods were used to estimate the number of sources in each filtered EEG dataset. Under the filter with the pass band of 1-30. Hz, the numbers of sources were very similar among different individual EEG datasets and the group ICA would be suggested; regarding the other two filters with much narrower pass bands, the numbers of sources were relatively diverse, and then, applying group ICA would not be appropriate. Hence, before group ICA is performed, its rationale can be logically validated by the estimated number of sources in EEG data through model order selection.

Original languageEnglish
Pages (from-to)165-172
Number of pages8
JournalJournal of Neuroscience Methods
Volume212
Issue number1
DOIs
Publication statusPublished - 2013
Externally publishedYes

Keywords

  • EEG
  • Event-related potential
  • Filter
  • Group component analysis
  • Mismatch negativity
  • Model order selection
  • Number of sources

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