ue to the volume conduction multichannel electroencephalogram (EEG) recordings

give a rather blurred image of brain activity. Therefore spatial filters are

extremely useful in single-trial analysis in order to improve the signal-to-noise

ratio. There are powerful methods from machine learning and signal processing

that permit the optimization of spatio-temporal filters for each subject in a data

dependent fashion beyond the fixed filters based on the sensor geometry, e.g., Laplacians. Here

we elucidate the theoretical background of the common spatial pattern (CSP) algorithm, a popular

method in brain-computer interface (BCI) research. Apart from reviewing several variants of

the basic algorithm, we reveal tricks of the trade for achieving a powerful CSP performance,

briefly elaborate on theoretical aspects of CSP, and demonstrate the application of CSP-type preprocessing

in our studies of the Berlin BCI (BBCI) project.