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  Robust EEG Channel Selection Across Subjects for Brain Computer Interfaces

Schröder, M., Lal, T., Hinterberger T, Bogdan M, Hill, J., Birbaumer N, Rosenstiel, W., & Schölkopf, B. (2005). Robust EEG Channel Selection Across Subjects for Brain Computer Interfaces. EURASIP Journal on Applied Signal Processing, 2005(19, Special Issue: Trends in Brain Computer Interfaces), 3103-3112. doi:10.1155/ASP.2005.3103.

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 Creators:
Schröder, M1, Author           
Lal, TN2, Author           
Hinterberger T, Bogdan M, Hill, J2, Author           
Birbaumer N, Rosenstiel, W, Author
Schölkopf, B2, Author           
Vesin T. Ebrahimi, J. M., Editor
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 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.

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 Dates: 2005
 Publication Status: Issued
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Title: EURASIP Journal on Applied Signal Processing
Source Genre: Journal
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Pages: - Volume / Issue: 2005 (19, Special Issue: Trends in Brain Computer Interfaces) Sequence Number: - Start / End Page: 3103 - 3112 Identifier: -