The Berlin Brain-Computer Interface

  • Benjamin Blankertz
  • Michael Tangermann
  • Florin Popescu
  • Matthias Krauledat
  • Siamac Fazli
  • Márton Dónaczy
  • Gabriel Curio
  • Klaus-Robert Müller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5050)


The Berlin Brain-Computer Interface (BBCI) uses a machine learning approach to extract subject-specific patterns from high-dimensional EEG-features optimized for revealing the user’s mental state. Classical BCI application are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([2] and see [3,4,5,6] for an overview on BCI). In these applications the BBCI uses natural motor competences of the users and specifically tailored pattern recognition algorithms for detecting the user’s intent. But beyond rehabilitation, there is a wide range of possible applications in which BCI technology is used to monitor other mental states, often even covert ones (see also [7] in the fMRI realm). While this field is still largely unexplored, two examples from our studies are exemplified in Section 4.3 and 4.4.


Linear Discriminant Analysis Brain Computer Interface Error Index Readiness Potential Common Spatial Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Benjamin Blankertz
    • 1
    • 2
  • Michael Tangermann
    • 1
  • Florin Popescu
    • 2
  • Matthias Krauledat
    • 1
  • Siamac Fazli
    • 2
  • Márton Dónaczy
    • 2
  • Gabriel Curio
    • 3
  • Klaus-Robert Müller
    • 1
    • 2
  1. 1.Berlin Institute of TechnologyMachine Learning LaboratoryBerlinGermany
  2. 2.Fraunhofer FIRST (IDA)BerlinGermany
  3. 3.Campus Benjamin FranklinCharité University Medicine BerlinGermany

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