A new (semantic) reflexive brain-computer interface: In search for a suitable classifier

A. Furdea, C. A. Ruf, S. Halder, D. De Massari, M. Bogdan, W. Rosenstiel, T. Matuz, N. Birbaumer

Research output: Contribution to journalArticlepeer-review


The goal of the current study is to find a suitable classifier for electroencephalogram (EEG) data derived from a new learning paradigm which aims at communication in paralysis. A reflexive semantic classical (Pavlovian) conditioning paradigm is explored as an alternative to the operant learning paradigms, currently used in most brain-computer interfaces (BCIs). Comparable with a lie-detection experiment, subjects are presented with true and false statements. The EEG activity following true and false statements was classified with the aim to separate covert 'yes' from covert 'no' responses. Four classification algorithms are compared for classifying off-line data collected from a group of 14 healthy participants: (i) stepwise linear discriminant analysis (SWLDA), (ii) shrinkage linear discriminant analysis (SLDA), (iii) linear support vector machine (LIN-SVM) and (iv) radial basis function kernel support vector machine (RBF-SVM).The results indicate that all classifiers perform at chance level when separating conditioned 'yes' from conditioned 'no' responses. However, single conditioned reactions could be successfully classified on a single-trial basis (single conditioned reaction against a baseline interval). All of the four investigated classification methods achieve comparable performance, however results with RBF-SVM show the highest single-trial classification accuracy of 68.8%. The results suggest that the proposed paradigm may allow affirmative and negative (disapproving negative) communication in a BCI experiment.

Original languageEnglish
Pages (from-to)233-240
Number of pages8
JournalJournal of Neuroscience Methods
Issue number1
Publication statusPublished - Jan 15 2012


  • Brain-computer interface
  • Classical semantic conditioning
  • Electroencephalogram
  • Single-trial classification

ASJC Scopus subject areas

  • Neuroscience(all)


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