Evaluation of PCA and ICA of simulated ERPs: Promax vs. infomax rotations
Article first published online: 28 NOV 2006
Copyright © 2006 Wiley-Liss, Inc.
Human Brain Mapping
Volume 28, Issue 8, pages 742–763, August 2007
How to Cite
Dien, J., Khoe, W. and Mangun, G. R. (2007), Evaluation of PCA and ICA of simulated ERPs: Promax vs. infomax rotations. Hum. Brain Mapp., 28: 742–763. doi: 10.1002/hbm.20304
- Issue published online: 12 JUL 2007
- Article first published online: 28 NOV 2006
- Manuscript Accepted: 15 MAY 2006
- Manuscript Received: 15 AUG 2005
- National Institute of Mental Health (NIMH). Grant Numbers: MH11751, MH55714, MH02019
- principal components analysis;
- independent components analysis;
- event-related potentials
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. Hum Brain Mapp 2006. © 2006 Wiley-Liss, Inc.