Single-trial classification of Event-Related Potentials (ERPs) is needed in many real-world
brain-computer interface (BCI) applications. However, because of individual differences,
the classifier needs to be calibrated by using some labeled subject-specific training
samples, which may be inconvenient to obtain. In this paper we propose a weighted
adaptation regularization (wAR) approach for offline BCI calibration, which uses data
from other subjects to reduce the amount of labeled data required in offline single-trial
classification of ERPs. Our proposed model explicitly handles class-imbalance problems
which are common in many real-world BCI applications. wAR can improve the classification
performance, given the same number of labeled subject-specific training samples; or,
equivalently, it can reduce the number of labeled subject-specific training samples,
given a desired classification accuracy. To reduce the computational cost of wAR,
we also propose a source domain selection (SDS) approach. Our experiments show that
wARSDS can achieve comparable performance with wAR but is much less computationally
intensive. We expect wARSDS to find broad applications in offline BCI calibration.
