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Optimizing spatial filters for robust EEG single-trial analysis

delete2008-01-01
delete1.6K
PRE
AI
B
Benjamin Blankertz *
R
Ryota Tomioka
S
Steven Lemm
M
Motoaki Kawanabe
K
Klaus-Robert Müller
DOI：10.1109/MSP.2008.4408441delete
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Abstract

Abstract

En 中文
Due 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.
Keywords:
BRAIN-COMPUTER INTERFACE
MOTOR IMAGERY
CLASSIFICATION
COMMUNICATION
EXTRACTION
DYNAMICS
RHYTHMS

Journal

IEEE Signal Processing Magazine cover
IEEE Signal Processing Magazine
IF:
9.6
Papers:
1.1W
Citations:
1.7W

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No organization information available