EURASIP Journal on Applied Signal Processing 
Volume 2005 (2005), Issue 19, Pages 3103-3112
doi:10.1155/ASP.2005.3103

Robust EEG Channel Selection across Subjects for Brain-Computer Interfaces

Michael Schröder,1 Thomas Navin Lal,2 Thilo Hinterberger,3 Martin Bogdan,1 N. Jeremy Hill,2 Niels Birbaumer,3 Wolfgang Rosenstiel,1 and Bernhard Schölkopf2

 1Department of Computer Engineering, Eberhard-Karls University Tübingen, Sand 13, Tübingen 72076, Germany
2Max Planck Institute for Biological Cybernetics, Spemannstrasse 38, Tübingen 72076, Germany
3Institute of Medical Psychology and Behavioral Neurobiology, Eberhard-Karls University Tübingen, Gartenstrasse 29, Tübingen 72074, Germany
Received 11 February 2004; Revised 22 September 2004

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.