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Robust EEG Channel Selection across Subjects for Brain-Computer Interfaces

Abstract

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.

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Correspondence to Michael Schröder.

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Schröder, M., Lal, T.N., Hinterberger, T. et al. Robust EEG Channel Selection across Subjects for Brain-Computer Interfaces. EURASIP J. Adv. Signal Process. 2005, 174746 (2005). https://doi.org/10.1155/ASP.2005.3103

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Keywords and phrases:

  • brain-computer interface
  • channel selection
  • feature selection
  • recursive channel elimination
  • support vector machine
  • electroencephalography