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Abstract

Brain-computer interfaces (BCIs) are limited
in their applicability in everyday settings
by the current necessity to record subjectspecific
calibration data prior to actual use
of the BCI for communication. In this paper,
we utilize the framework of multitask
learning to construct a BCI that can be used
without any subject-specific calibration process.
We discuss how this out-of-the-box BCI
can be further improved in a computationally
efficient manner as subject-specific data
becomes available. The feasibility of the approach
is demonstrated on two sets of experimental
EEG data recorded during a standard
two-class motor imagery paradigm from
a total of 19 healthy subjects. Specifically,
we show that satisfactory classification results
can be achieved with zero training data,
and combining prior recordings with subjectspecific
calibration data substantially outperforms
using subject-specific data only. Our
results further show that transfer between
recordings under slightly different experimental
setups is feasible.