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Evaluating two-step PCA of ERP data with Geomin, Infomax, Oblimin, Promax, and Varimax rotations

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  • This work was supported by NASA Grant SA23-06-015. Thanks to the editor and to the anonymous reviewer for their very helpful comments.

Address reprint requests to: Joseph Dien, Center for the Advanced Study of Language, University of Maryland, Box 25, College Park, MD 20742-0025, USA. E-mail: jdien07@mac.com

Abstract

Principal components analysis (PCA) can facilitate analysis of event-related potential (ERP) components. Geomin, Oblimin, Varimax, Promax, and Infomax (independent components analysis) were compared using a simulated data set. Kappa settings for Oblimin and Promax were also systematically compared. Finally, the rotations were also analyzed in a two-step PCA procedure, including a contrast between spatiotemporal and temporospatial procedures. Promax was found to give the best overall results for temporal PCA, and Infomax was found to give the best overall results for spatial PCA. The current practice of kappa values of 3 or 4 for Promax and 0 for Oblimin was supported. Source analysis was meaningfully improved by temporal Promax PCA over the conventional windowed difference wave approach (from a median 32.9 mm error to 6.7 mm). It was also found that temporospatial PCA produced modestly improved results over spatiotemporal PCA.

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