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

263 Citations (Scopus)

Abstract

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
Volume14
Issue number2
DOIs
Publication statusPublished - 2006 Jun

Bibliographical note

Funding Information:
Manuscript received July 19, 2005; revised March 21, 2006; March 22, 2006. This work was supported in part by grants of the Bundesministerium für Bil-dung und Forschung (BMBF), FKZ 01IBE01A/B, by the Deutsche Forschungs-gemeinschaft (DFG), FOR 375/B1, and in part by the IST Programme of the European Community, under the PASCAL Network of Excellence, IST-2002-506778. This publication only reflects the authors’ views.

Keywords

  • 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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