Logistic regression for single trial EEG classification

Ryota Tomioka, Kazuyuki Aihara, Klaus Muller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

58 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems
Pages1377-1384
Number of pages8
Publication statusPublished - 2007 Dec 1
Externally publishedYes
Event20th Annual Conference on Neural Information Processing Systems, NIPS 2006 - Vancouver, BC, Canada
Duration: 2006 Dec 42006 Dec 7

Other

Other20th Annual Conference on Neural Information Processing Systems, NIPS 2006
CountryCanada
CityVancouver, BC
Period06/12/406/12/7

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Tomioka, R., Aihara, K., & Muller, K. (2007). Logistic regression for single trial EEG classification. In Advances in Neural Information Processing Systems (pp. 1377-1384)