文件名称:Optimizing-Spatial-Filters-for-Robust-EEG-Single-
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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.
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.
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Optimizing Spatial Filters for Robust EEG Single-Trial Analysis- blankertz-2008.pdf