The time-frequency PCA decomposition (TF-PCA) method is detailed in Bernat et al. (2005, also see E. M. Bernat et al., 2007). Here, the primary features are outlined. The data handling and decomposition steps were carried out in Matlab (version 6.5, Mathworks, Inc.) using a generalized set of scripts developed for this purpose1. All TF transforms were computed using Cohen’s class RID transform. TF transforms were created using the entire, baseline corrected (−500 to −10 ms), 2 s epoch to allow for rejection of edge effects from the transform. PCA was then performed on the resulting TF surfaces to decompose the surfaces into TF components. PCA applied to TF energy much resembles its application to signals in the time or frequency domain. First, TF surfaces are rearranged into vectors, recasting the TF energy into a matrix with subjects in rows (or trials if one were performing trial-level decomposition) and time-frequency energy points in columns. Then, the covariance matrix is decomposed, varimax rotation is applied to maximize simple structure, and the component vectors are rearranged back into surfaces representing each TF-PCA component’s
