Multisubject learning for common spatial patterns in motor-imagery BCI

  • Authors:

  • Dieter Devlaminck;Bart Wyns;Moritz Grosse-Wentrup;Georges Otte;Patrick Santens

  • Affiliations:

  • Electrical Energy, Systems and Automation, Ghent University, Gent, Belgium;Electrical Energy, Systems and Automation, Ghent University, Gent, Belgium;Department of Empirical Inference,Max Planck Institute for Biological Cybernetics, Tübingen, Germany;P.C. Dr. Guislain, Gent, Belgium;Department of Neurology, Ghent University Hospital, Gent, Belgium

  • Venue:
  • Computational Intelligence and Neuroscience - Special issue on Selected Papers from the 4th International Conference on Bioinspired Systems and Cognitive Signal Processing
  • Year:
  • 2011

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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.