Open AccessEvaluation of PCA and ICA of simulated ERPs: Promax vs. infomax rotations
Author(s) -
Dien Joseph,
Khoe Wayne,
Mangun George R.
Publication year - 2007
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.20304
Subject(s) - infomax , independent component analysis , principal component analysis , pattern recognition (psychology) , electroencephalography , artificial intelligence , psychology , computer science , speech recognition , neuroscience , blind signal separation , computer network , channel (broadcasting)
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.