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Open Access

Robust EEG Channel Selection across Subjects for Brain-Computer Interfaces

  • Michael Schröder1Email author,
  • Thomas Navin Lal2,
  • Thilo Hinterberger3,
  • Martin Bogdan1,
  • N. Jeremy Hill2,
  • Niels Birbaumer3,
  • Wolfgang Rosenstiel1 and
  • Bernhard Schölkopf2
EURASIP Journal on Advances in Signal Processing20052005:174746

Received: 11 February 2004

Published: 17 November 2005


Most EEG-based brain-computer interface (BCI) paradigms come along with specific electrode positions, for example, for a visual-based BCI, electrode positions close to the primary visual cortex are used. For new BCI paradigms it is usually not known where task relevant activity can be measured from the scalp. For individual subjects, Lal et al. in 2004 showed that recording positions can be found without the use of prior knowledge about the paradigm used. However it remains unclear to what extent their method of recursive channel elimination (RCE) can be generalized across subjects. In this paper we transfer channel rankings from a group of subjects to a new subject. For motor imagery tasks the results are promising, although cross-subject channel selection does not quite achieve the performance of channel selection on data of single subjects. Although the RCE method was not provided with prior knowledge about the mental task, channels that are well known to be important (from a physiological point of view) were consistently selected whereas task-irrelevant channels were reliably disregarded.

Keywords and phrases:

brain-computer interfacechannel selectionfeature selectionrecursive channel eliminationsupport vector machineelectroencephalography

Authors’ Affiliations

Department of Computer Engineering, Eberhard-Karls University Tübingen, Tübingen, Germany
Max Planck Institute for Biological Cybernetics, Tübingen, Germany
Institute of Medical Psychology and Behavioral Neurobiology, Eberhard-Karls University Tübingen, Tübingen, Germany


© Schröder et al. 2005