The Berlin brain - computer interface: Accurate performance from first-session in BCI-naïve subjects

Benjamin Blankertz, Florian Losch, Matthias Krauledat, Guido Dornhege, Gabriel Curio, Klaus Robert Müller

Research output: Contribution to journalArticlepeer-review

273 Citations (Scopus)


The Berlin Brain - Computer Interface (BBCI) project develops a noninvasive BCI system whose key features are: 1) the use of well-established motor competences as control paradigms; 2) high-dimensional features from multichannel EEG; and 3) advanced machine-learning techniques. Spatio-spectral changes of sensorimotor rhythms are used to discriminate imagined movements (left hand, right hand, and foot). A previous feedback study [M. Krauledat, K.-R. Müller, and G. Curio. (2007) The non-invasive Berlin brain - computer Interface: Fast acquisition of effective performance in untrained subjects. NeuroImage. [Online]. 37(2), pp. 539 - 550. Available: 1016/j.neuroimage.2007.01.051] with ten subjects provided preliminary evidence that the BBCI system can be operated at high accuracy for subjects with less than five prior BCI exposures. Here, we demonstrate in a group of 14 fully BCI-naïve subjects that 8 out of 14 BCI novices can perform at > 84% accuracy in their very first BCI session, and a further four subjects at > 70%. Thus, 12 out of 14 BCI-novices had significant above-chance level performances without any subject training even in the first session, as based on an optimized EEG analysis by advanced machine-learning algorithms.

Original languageEnglish
Article number18
Pages (from-to)2452-2462
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
Issue number10
Publication statusPublished - 2008 Oct

Bibliographical note

Funding Information:
Manuscript received September 10, 2007; revised January 26, 2008. First published June 10, 2008; current version published September 26, 2008. This work was supported in part by the Bundesministerium für Bildung und Forschung (BMBF) under Grant FKZ 01IBE01A/B and in part by the Information Society Technologies (IST) Programme of the European Community under the PASCAL Network of Excellence, IST-2002-506778. This publication only reflects the authors’ views. Asterisk indicates corresponding author.


  • Brain-computer interface
  • Common spatial pattern analysis
  • Electroencephalography
  • Event-related desynchronization
  • Machine learning
  • Pattern classification
  • Sensorymotor rhythms
  • Single-trial analysis

ASJC Scopus subject areas

  • Biomedical Engineering


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