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Computational Intelligence and Neuroscience
Volume 2011, Article ID 217987, 9 pages
Research Article

Multisubject Learning for Common Spatial Patterns in Motor-Imagery BCI

1Electrical Energy, Systems and Automation, Ghent University, Technologiepark 913, Zwijnaarde, 9052 Gent, Belgium
2Department of Empirical Inference, Max Planck Institute for Biological Cybernetics, Spemannstr. 38, 72076 Tübingen, Germany
3P.C. Dr. Guislain, Fr. Ferrerlaan 88A, 9000 Gent, Belgium
4Department of Neurology, Ghent University Hospital, De Pintelaan 185, 9000 Gent, Belgium

Received 14 March 2011; Revised 28 July 2011; Accepted 29 July 2011

Academic Editor: Fabio Babiloni

Copyright © 2011 Dieter Devlaminck et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.