Evaluation of PCA and ICA of simulated ERPs: Promax vs. infomax rotations

Joseph Dien, Wayne Khoe, George R. Mangun

Research output: Contribution to journalArticlepeer-review

129 Scopus citations


Independent components analysis (ICA) and principal components analysis (PCA) are methods used to analyze event-related potential (ERP) and functional imaging (fMRI) data. In the present study, ICA and PCA were directly compared by applying them to simulated ERP datasets. Specifically, PCA was used to generate a subspace of the dataset followed by the application of PCA Promax or ICA Infomax rotations. The simulated datasets were composed of real background EEG activity plus two ERP simulated components. The results suggest that Promax is most effective for temporal analysis, whereas Infomax is most effective for spatial analysis. Failed analyses were examined and used to devise potential diagnostic strategies for both rotations. Finally, the results also showed that decomposition of subject averages yield better results than of grand averages across subjects.

Original languageEnglish (US)
Pages (from-to)742-763
Number of pages22
JournalHuman Brain Mapping
Issue number8
StatePublished - Aug 2007
Externally publishedYes


  • Event-related potentials
  • Independent components analysis
  • Principal components analysis

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology


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