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 matrix of rotated component loadings for each TF point. The number of components to extract was determined by inspecting the scree plot of singular values, representing the relative variance accounted for by each component, for a break, or elbow. Finally, each subject’s TF surface is weighted using the extracted TF-PCA components. To weight the original TF data, each time-frequency point is multiplied by the corresponding point in the matrix of rotated loadings for each component. This produces weighted data surfaces, for each subject for each TF-PCA component, whose data points represent energy in units weighted by the component loadings. For statistical analyses, component scores representing the peak energy on the weighted TF data surface (i.e. the time-frequency point with the highest energy) was used. This method allowed comparison of analogous measures from the TF (peak energy) and time (peak P3 amplitude) domains.
