Brain Topography

, Volume 23, Issue 2, pp 186–193 | Cite as

On Optimal Channel Configurations for SMR-based Brain–Computer Interfaces

  • Claudia Sannelli
  • Thorsten Dickhaus
  • Sebastian Halder
  • Eva-Maria Hammer
  • Klaus-Robert Müller
  • Benjamin Blankertz
Original Paper


One crucial question in the design of electroencephalogram (EEG)-based brain–computer interface (BCI) experiments is the selection of EEG channels. While a setup with few channels is more convenient and requires less preparation time, a dense placement of electrodes provides more detailed information and henceforth could lead to a better classification performance. Here, we investigate this question for a specific setting: a BCI that uses the popular CSP algorithm in order to classify voluntary modulations of sensorimotor rhythms (SMR). In a first approach 13 different fixed channel configurations are compared to the full one consisting of 119 channels. The configuration with 48 channels results to be the best one, while configurations with less channels, from 32 to 8, performed not significantly worse than the best configuration in cases where only few training trials are available. In a second approach an optimal channel configuration is obtained by an iterative procedure in the spirit of stepwise variable selection with nonparametric multiple comparisons. As a surprising result, in the second approach a setting with 22 channels centered over the motor areas was selected. Thanks to the acquisition of a large data set recorded from 80 novice participants using 119 EEG channels, the results of this study can be expected to have a high degree of generalizability.


Brain–computer interface Common spatial patterns Channel selection EEG Machine learning Variable selection 



This work was supported in part by grants of the Deutsche Forschungsgemeinschaft (DFG), MU 987/3-1 - KU 1453-1, Bundesministerium für Bildung und Forschung (BMBF), FKZ 01IB001A/B - 01GQ0850 and by the FP7-ICT Programme of the European Community FP7-224631 - FP7-216886.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Claudia Sannelli
    • 1
  • Thorsten Dickhaus
    • 1
  • Sebastian Halder
    • 2
  • Eva-Maria Hammer
    • 2
  • Klaus-Robert Müller
    • 1
  • Benjamin Blankertz
    • 1
    • 3
  1. 1.Machine Learning LaboratoryBerlin Institute of TechnologyBerlinGermany
  2. 2.Institute of Medical Psychology and Behavioral NeurobiologyUniversity of TübingenTübingenGermany
  3. 3.Intelligent Data Analysis Group, Fraunhofer FIRSTBerlinGermany

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