Optimizing spatial filters for robust EEG single-trial analysis

Benjamin Blankertz; Ryota Tomioka; Steven Lemm; Motoaki Kawanabe; Klaus Robert Müller

doi:10.1109/MSP.2008.4408441

Optimizing spatial filters for robust EEG single-trial analysis

Benjamin Blankertz, Ryota Tomioka, Steven Lemm, Motoaki Kawanabe, Klaus Robert Müller

Research output: Contribution to journal › Article › peer-review

1372 Citations (Scopus)

Abstract

The common spatial pattern (CSP) provides a clearer image of brain activity in single-trial analysis and improves signal-to-noise ratio in conducting multichannel electroencephalogram (EEG) recordings. CSP is a popular method in brain-computer interface (BCI) research. BCI systems promise developments in usability, information transfer and robustness for which modern machine learning and signal processing techniques have been instrumental. CSP produces a data-driven supervised decomposition of the signal parameterized by a certain matrix and is used to analyze multichannel data based on recordings from two conditions. CSP filters maximize the variance of the spatially filtered signal under one condition while minimizing it for another condition and can be applicable to band-pass filtered signals in order to yield an effective discrimination of mental stats that are characterized by ERD/ERS (event-related desynchronizationevent-related synchronization) effects. CSP can be used to extract general discriminative spatio-temporal structure for multivariate data streams beyond EEG.

Original language	English
Pages (from-to)	41-56
Number of pages	16
Journal	IEEE Signal Processing Magazine
Volume	25
Issue number	1
DOIs	https://doi.org/10.1109/MSP.2008.4408441
Publication status	Published - 2008 Jan

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

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10.1109/MSP.2008.4408441

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title = "Optimizing spatial filters for robust EEG single-trial analysis",

abstract = "The common spatial pattern (CSP) provides a clearer image of brain activity in single-trial analysis and improves signal-to-noise ratio in conducting multichannel electroencephalogram (EEG) recordings. CSP is a popular method in brain-computer interface (BCI) research. BCI systems promise developments in usability, information transfer and robustness for which modern machine learning and signal processing techniques have been instrumental. CSP produces a data-driven supervised decomposition of the signal parameterized by a certain matrix and is used to analyze multichannel data based on recordings from two conditions. CSP filters maximize the variance of the spatially filtered signal under one condition while minimizing it for another condition and can be applicable to band-pass filtered signals in order to yield an effective discrimination of mental stats that are characterized by ERD/ERS (event-related desynchronizationevent-related synchronization) effects. CSP can be used to extract general discriminative spatio-temporal structure for multivariate data streams beyond EEG.",

author = "Benjamin Blankertz and Ryota Tomioka and Steven Lemm and Motoaki Kawanabe and M{\"u}ller, {Klaus Robert}",

note = "Funding Information: This work was supported in part by grants of the Bundesministerium f{\"u}r Bildung und Forschung (BMBF), FKZ 01IBE01A (BCI III) and 01GQ0415 (BCCNB-A4), by MEXT, Grant-in-Aid for JSPS fellows, 17-11866 and by the IST Programme of the European Community, under the PASCAL Network of Excellence, IST-2002-506778. This publication only reflects the authors{\textquoteright} views.",

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Optimizing spatial filters for robust EEG single-trial analysis

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