Reducing BCI calibration effort in RSVP tasks using online weighted adaptation regularization with source domain selection

Rapid serial visual presentation based brain-computer interface (BCI) system relies on single-trial classification of event-related potentials. Because of large individual differences, some labeled subject-specific data are needed to calibrate the classifier for each new subject. This paper proposes an online weighted adaptation regularization (OwAR) algorithm to reduce the online calibration effort, and hence to increase the utility of the BCI system. We show that given the same number of labeled subject-specific training samples, OwAR can significantly improve the online calibration performance. In other words, given a desired classification accuracy, OwAR can significantly reduce the number of labeled subject-specific training samples. Furthermore, we also show that the computational cost of OwAR can be reduced by more than 50% by source domain selection, without a statistically significant sacrifice of classification performance.