The Berlin brain-computer interface: EEG-based communication without subject training

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

Research output: Contribution to journalArticlepeer-review

250 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 128-channel electroencephalogram (EEG), and 3) advanced machine learning techniques. As reported earlier, our experiments demonstrate that very high information transfer rates can be achieved using the readiness potential (RP) when predicting the laterality of upcoming left- versus right-hand movements in healthy subjects. A more recent study showed that the RP similarity accompanies phantom movements in arm amputees, but the signal strength decreases with longer loss of the limb. In a complementary approach, oscillatory features are used to discriminate imagined movements (left hand versus right hand versus foot). In a recent feedback study with six healthy subjects with no or very little experience with BCI control, three subjects achieved an information transfer rate above 35 bits per minute (bpm), and further two subjects above 24 and 15 bpm, while one subject could not achieve any BCI control. These results are encouraging for an EEG-based BCI system in untrained subjects that is independent of peripheral nervous system activity and does not rely on evoked potentials even when compared to results with very well-trained subjects operating other BCI systems.

Original languageEnglish
Article number1642756
Pages (from-to)147-152
Number of pages6
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number2
Publication statusPublished - 2006 Jun


  • Brain-computer interface (BCI)
  • Classification
  • Common spatial patterns
  • Electroencephalogram (EEG)
  • Event-related desynchronization (ERD)
  • Information transfer rate
  • Machine learning
  • Readiness potential (RP)
  • Single-trial analysis

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering


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