PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Robust EEG Channel Selection Across Subjects for Brain Computer Interfaces
M. Schroeder, Thomas Navin Lal, T. Hinterberger, M. Bogdan, N.J. Hill, N. Birbaumer, W. Rosenstiel and Bernhard Schölkopf
EURASIP Journal on Applied Signal Processing 2004.

Abstract

Most EEG-based Brain Computer Interface (BCI) paradigms come along with specific electrode positions, e.g.~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 showed that recording positions can be found without the use of prior knowledge about the paradigm used. However it remains unclear to what extend 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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:406
Deposited By:Thomas Navin Lal
Deposited On:19 December 2004