| Summary: | 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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