PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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 Volume 25, Number 1, pp. 41-56, 2008.


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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:3318
Deposited By:Benjamin Blankertz
Deposited On:07 February 2008