Slow Feature Analysis as a Potential Preprocessing Tool in BCI
Here we present initial results of the unsupervised preprocessing method Slow Feature Analysis (SFA) for a BCI data set. It is the first time SFA is applied to EEG. SFA optimizes the signal representation with respect to temporal slowness. Its objective as well as its computational properties render it a possibly useful candidate for the preprocessing of BCI EEG data in order to detect task relevant components as well as components that represent artifacts or non-stationarities of the background brain activity or external sources.