Logistic regression for single trial EEG classification

Ryota Tomioka, Kazuyuki Aihara, Klaus Robert Müller

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

80 Citations (Scopus)


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 19 - Proceedings of the 2006 Conference
Number of pages8
Publication statusPublished - 2007
Event20th Annual Conference on Neural Information Processing Systems, NIPS 2006 - Vancouver, BC, Canada
Duration: 2006 Dec 42006 Dec 7

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Other20th Annual Conference on Neural Information Processing Systems, NIPS 2006
CityVancouver, BC

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


Dive into the research topics of 'Logistic regression for single trial EEG classification'. Together they form a unique fingerprint.

Cite this