Slow feature analysis (SFA) is a method for extracting slowly varying driving
forces from quickly varying nonstationary time series. We show here that it is
possible for SFA to detect a component which is even slower than the driving
force itself (e.g. the envelope of a modulated sine wave). It is shown that it
depends on circumstances like the embedding dimension, the time series
predictability, or the base frequency, whether the driving force itself or a
slower subcomponent is detected.