PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Logistic regression for single trial EEG classification
Ryota Tomioka, Kazuyuki Aihara and Klaus-Robert Müller
Advances in Neural Information Processing Systems Volume 19, pp. 1377-1384, 2007.

Abstract

We propose a novel framework for the classification of single trial ElectroEncephaloGraphy (EEG), based on regularized logistic regression. Framed in this robust statistical framework no prior feature extraction or outlier removal is required. We present two variations of parameterizing the regression function: (a) with a full rank symmetric matrix coefficient and (b) as a difference of two rank=1 matrices. In the first case, the problem is convex and the logistic regression is optimal under a generative model. The latter case is shown to be related to the Common Spatial Pattern (CSP) algorithm, which is a popular technique in Brain Computer Interfacing. The regression coefficients can also be topographically mapped onto the scalp similarly to CSP projections, which allows neuro-physiological interpretation. Simulations on 162 BCI datasets demonstrate that classification accuracy and robustness compares favorably against conventional CSP based classifiers.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:3315
Deposited By:Benjamin Blankertz
Deposited On:07 February 2008