The BCI competition III: Validating alternative approaches to actual BCI problems

Benjamin Blankertz, Klaus Robert Müller, Dean J. Krusienski, Gerwin Schalk, Jonathan R. Wolpaw, Alois Schlögl, Gert Pfurtscheller, José Del R. Millán, Michael Schröder, Niels Birbaumer

Research output: Contribution to journalArticlepeer-review

687 Citations (Scopus)


A brain-computer interface (BCI) is a system that allows its users to control external devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.

Original languageEnglish
Article number1642757
Pages (from-to)153-159
Number of pages7
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number2
Publication statusPublished - 2006 Jun


  • Augmentative communication
  • Beta rhythm
  • Brain-computer interface (BCI)
  • ERP
  • Electroencephalography (EEG)
  • Imagined hand movements
  • Mu rhythm
  • Nonstationarity
  • P300
  • Rehabilitation
  • Single-trial classification
  • Slow cortical potentials

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering


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