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
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.