A regularized discriminative framework for EEG analysis with application to brain-computer interface

Ryota Tomioka, Klaus Robert Müller

Research output: Contribution to journalArticlepeer-review

135 Citations (Scopus)


We propose a framework for signal analysis of electroencephalography (EEG) that unifies tasks such as feature extraction, feature selection, feature combination, and classification, which are often independently tackled conventionally, under a regularized empirical risk minimization problem. The features are automatically learned, selected and combined through a convex optimization problem. Moreover we propose regularizers that induce novel types of sparsity providing a new technique for visualizing EEG of subjects during tasks from a discriminative point of view. The proposed framework is applied to two typical BCI problems, namely the P300 speller system and the prediction of self-paced finger tapping. In both datasets the proposed approach shows competitive performance against conventional methods, while at the same time the results are easier accessible to neurophysiological interpretation. Note that our novel approach is not only applicable to Brain imaging beyond EEG but also to general discriminative modeling of experimental paradigms beyond BCI.

Original languageEnglish
Pages (from-to)415-432
Number of pages18
Issue number1
Publication statusPublished - 2010 Jan 1


  • Brain-computer interface
  • Convex optimization
  • Discriminative learning
  • Discriminative modeling of brain imaging signals
  • Dual spectral norm
  • Group-lasso
  • P300 speller
  • Regularization
  • Spatio-temporal factorization
  • Trace norm

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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