Summary
This paper discusses machine learning methods and their application to Brain-Computer Interfacing. A particular focus is placed on linear classification methods which can be applied in the BCI context. Finally, we provide an overview on the Berlin-Brain Computer Interface (BBCI).
Keywords
- Linear Discriminant Analysis
- Motor Imagery
- Lateralized Readiness Potential
- Strong Noise
- Information Transfer Rate
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.
The studies were partly supported by the Bundesministerium für Bildung und Forschung (BMBF), FKZ 01IBB02A and FKZ and FKZ 01IBB02B, by the Deutsche Forschungsgemeinschaft (DFG), FOR 375/B1 and the PASCAL Network of Excellence, EU #506778. Thispaper is based on excerpts of [1].
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Müller, K.R., Krauledat, M., Dornhege, G., Jähnichen, S., Curio, G., Blankertz, B. (2006). A note on the Berlin Brain-Computer Interface. In: Hommel, G., Huanye, S. (eds) Human Interaction with Machines. Springer, Dordrecht . https://doi.org/10.1007/1-4020-4043-1_6
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DOI: https://doi.org/10.1007/1-4020-4043-1_6
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