PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Spatio-Spectral Filter for Improved Classification of Single-Trial EEG
Steven Lemm, Benjamin Blankertz, Gabriel Curio and Klaus-Robert Müller
IEEE Trans. Biomed. Eng. Volume 52, Number 9, pp. 1541-1548, 2005.

Abstract

Data recorded in EEG based Brain-Computer Interfacing experiments is generally very noisy, nonstationary and contaminated with artifacts, that can deteriorate discrimination/classification methods. In this work we extend the Common Spatial Pattern (CSP) algorithm with the aim to alleviate these adverse effects. In particular we suggest an extension of CSP to the state space, which utilizes the method of delay embedding. As we will show, this allows for individually tuned frequency filters at each electrode position and thus yields an improved and more robust machine learning procedure. The advantages of the proposed method over the currently state-of-the-art method (CSP) are verified in terms of an improved information transfer rate (bits per trial) on a set of EEG-recordings from experiments of imagined limb movements.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:1533
Deposited By:Benjamin Blankertz
Deposited On:28 November 2005