  Brain signal variability in the measurements obtained from different subjects
during different sessions significantly deteriorates the accuracy of most
brain-computer interface (BCI) systems. Moreover these variabilities, also
known as inter-subject or inter-session variabilities, require lengthy
calibration sessions before the BCI system can be used. Furthermore, the
calibration session has to be repeated for each subject independently and
before use of the BCI due to the inter-session variability. In this study, we
present an algorithm in order to minimize the above-mentioned variabilities and
to overcome the time-consuming and usually error-prone calibration time. Our
algorithm is based on linear programming support-vector machines and their
extensions to a multiple kernel learning framework. We tackle the inter-subject
or -session variability in the feature spaces of the classifiers. This is done
by incorporating each subject- or session-specific feature spaces into much
richer feature spaces with a set of optimal decision boundaries. Each decision
boundary represents the subject- or a session specific spatio-temporal
variabilities of neural signals. Consequently, a single classifier with
multiple feature spaces will generalize well to new unseen test patterns even
without the calibration steps. We demonstrate that classifiers maintain good
performances even under the presence of a large degree of BCI variability. The
present study analyzes BCI variability related to oxy-hemoglobin neural signals
measured using a functional near-infrared spectroscopy.
