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

760 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

Bibliographical note

Funding Information:
Manuscript received July 19, 2005 The work of B. Blankertz and K.–R. Müller was supported in part by the Bundesministerium für Bildung und Forschung (BMBF), under Grant FKZ 01IBE01A/B and by the Deutsche Forschungsgemeinschaft (DFG), under Grant FOR 375/B1. The work of D. J. Krusienski, G. Schalk, and J. R. Wolpaw was supported in part by the National Institutes of Health under Grants HD30146 (National Center for Medical Rehabilitation Research of the National Institute of Child Health and Human Development) and EB00856 (National Institute of Biomedical Imaging and Bioengineering and National Institute of Neurological Disorders and Stroke) and by the James S. McDonnell Foundation. The work of J. d. R. Millán was supported by the Swiss National Science Foundation under Grant NCCR “IM2.” The work of B. Blankertz, K.-R. Müller, and J. d. R. Millán was supported in part by the IST Programme of the European Community, under the PASCAL Network of Excellence, under Grant IST-2002-506778.


  • 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

  • Rehabilitation
  • General Neuroscience
  • Internal Medicine
  • Biomedical Engineering


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