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  Multi-subject learning for common spatial patterns in motor-imagery BCI

Devlaminck, D., Wyns, B., Grosse-Wentrup, M., Otte, G., & Santens, P. (2011). Multi-subject learning for common spatial patterns in motor-imagery BCI. Computational Intelligence and Neuroscience, 2011: 217987, pp. 1-9. doi:10.1155/2011/217987.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BAB8-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-B205-0
Genre: Journal Article

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 Creators:
Devlaminck, D, Author
Wyns, B, Author
Grosse-Wentrup, M1, 2, Author              
Otte, G, Author
Santens, P, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern filter (CSP) as preprocessing step before feature extraction and classification. The CSP method is a supervised algorithm and therefore needs subject-specific training data for calibration, which is very time consuming to collect. In order to reduce the amount of calibration data that is needed for a new subject, one can apply multitask (from now on called multisubject) machine learning techniques to the preprocessing phase. Here, the goal of multisubject learning is to learn a spatial filter for a new subject based on its own data and that of other subjects. This paper outlines the details of the multitask CSP algorithm and shows results on two data sets. In certain subjects a clear improvement can be seen, especially when the number of training trials is relatively low.

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 Dates: 2011-08
 Publication Status: Published in print
 Pages: -
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 Rev. Method: -
 Identifiers: DOI: 10.1155/2011/217987
BibTex Citekey: DevlaminckWGOS2011
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Title: Computational Intelligence and Neuroscience
  Abbreviation : Comput Intell Neurosci
Source Genre: Journal
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Publ. Info: Hindawi : New York, NY
Pages: - Volume / Issue: 2011 Sequence Number: 217987 Start / End Page: 1 - 9 Identifier: ISSN: 1687-5265
CoNE: https://pure.mpg.de/cone/journals/resource/1687-5265