The decomposition of data in terms of a set of basis functions is often very useful in understanding the complicated response of a physical system. By decomposing into less complicated patterns, it may be easier to understand and shed light into the nature of the variability in a dataset. The concept of cyclostationary empirical orthogonal functions (CSEOFs) was introduced in an attempt to more compactly capture the time-varying spatial patterns and longer-timescale fluctuations present in geophysical signals when compared to traditional empirical orthogonal function (EOF) analysis. Here, we discuss CSEOF analysis and compare and contrast to other EOF-based techniques. Previous applications of CSEOFs will be described to help motivate the possible extension of the technique into new areas.