Machine learning methods of the Berlin brain-computer interface

Carmen Vidaurre, Claudia Sannelli, Wojciech Samek, Sven Dähne, Klaus Muller

Research output: Contribution to journalArticle

3 Citations (Scopus)


This paper is a compilation of the most recent machine learning methods used in the Berlin Brain-Computer Interface. In the field of Brain-Computer Interfacing, machine learning has been mainly used to extract meaningful features from noisy signals of large dimensionality and to classify them to transform them into computer commands. Recently, our group developed different methods to deal with noisy, non-stationary and high dimensional signals. These approaches can be seen as variants of the algorithm Common Spatial Patterns (CSP). All of them outperform CSP in the different conditions for which they were developed.

Original languageEnglish
Pages (from-to)447-452
Number of pages6
Issue number20
Publication statusPublished - 2015 Sep 1


  • Adaptive systems
  • Brain-computer interfacing
  • Electroencephalogram
  • Motor imagery
  • Multimodal analysis
  • Non-stationary analysis

ASJC Scopus subject areas

  • Control and Systems Engineering

Fingerprint Dive into the research topics of 'Machine learning methods of the Berlin brain-computer interface'. Together they form a unique fingerprint.

  • Cite this

    Vidaurre, C., Sannelli, C., Samek, W., Dähne, S., & Muller, K. (2015). Machine learning methods of the Berlin brain-computer interface. IFAC-PapersOnLine, 28(20), 447-452.