Optimizing spatial filters for robust EEG single-trial analysis
Benjamin Blankertz, Ryota Tomioka, Steven Lemm, Motoaki Kawanabe and Klaus-Robert Müller
IEEE Signal Proc. Magazine
Due to the volume conduction multi-channel 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 Brain-Computer Interface project.