Nuisance Sources of Variance in Principal Components analysis of Event-Related Potentials

This paper describes a method of disentangling different sources of variance contributing to component extraction in Principal Components Analysis (PCA) of event-related potentials. Those sources not of interest for a given experiment may be easily discarded prior to component extraction. A real data example is presented for comparison of the different approaches, showing advantages for the new methods. They also exhibited more success in detecting experimental effects as shown in subsequent analysis of variance procedures on component scores. In the latter framework, various issues of validity of subsequent testing procedures for all principal component approaches are addressed theoretically as well as empirically by a split-sample cross-validation study. It is claimed that data-adaptive computation of component scores does not constitute a crucial issue. Finally, a bootstrap simulation provides evidence that the methods proposed are superior to the usual PCA approach in capability and relibility in the assessment of experimental effects.