Recovering dipole sources from scalp-recorded event-related-potentials using component analysis: principal component analysis and independent component analysis.
Principal component analysis (PCA) and independent component analysis (ICA) were examined in their ability to recover dipole sources from simulated data. Datasets of EEG segments were generated that contained cortical sources that were temporally overlapping or non-overlapping, and dipole sources with varying degree of spatial orthogonality. For temporal overlapping sources, both PCA and ICA resulted in components that required multiple-source equivalent current dipole models. The spatially overlapping sources affected the PCA method more than ICA, resulting in single PCA components in which all non-orthogonal sources were represented. For both PCA and ICA, dipole models with fixed-location dipoles successfully accounted for most of the variance in the component weights, even when the spatial or temporal overlap of the generating sources required multiple-dipole models.
Department of Psychology, University of South Carolina, Columbia, SC 29208, USA. richards-john@sc.edu
John E
JE
Richards
D001931:Brain Mapping; D003198:Computer Simulation; D004569:Electroencephalography; D005071:Evoked Potentials; D006801:Humans; D008959:Models, Neurological; D025341:Principal Component Analysis; D012535:Scalp / Q000502:physiology*