Machine learning methods of the Berlin brain-computer interface

Carmen Vidaurre, Claudia Sannelli, Wojciech Samek, Sven Dähne, Klaus Robert Müller

Research output: Contribution to journalConference articlepeer-review

5 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 Sept 1
Event9th IFAC Symposium on Biological and Medical Systems, BMS 2015 - Berlin, Germany
Duration: 2015 Aug 312015 Sept 2

Bibliographical note

Funding Information:
The auffihors acknowledge funding ffly ffihe German Research Foundaffiion (DFG) granffi nos. MU 987/19-1, MU 987/14-1, MU 987/3-2, and supporffi ffly ffihe Bernsffiein Cenffier for Compuffiaffiional ∆euroscience Berlin ffihrough ffihe graduaffie program GRK 1589/1

Publisher Copyright:
© 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Copyright 2021 Elsevier B.V., All rights reserved.


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

ASJC Scopus subject areas

  • Control and Systems Engineering


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