  One big challenge that hinders the transition of brain-computer interfaces
(BCIs) from laboratory settings to real-life applications is the availability
of high-performance and robust learning algorithms that can effectively handle
individual differences, i.e., algorithms that can be applied to a new subject
with zero or very little subject-specific calibration data. Transfer learning
and domain adaptation have been extensively used for this purpose. However,
most previous works focused on classification problems. This paper considers an
important regression problem in BCI, namely, online driver drowsiness
estimation from EEG signals. By integrating fuzzy sets with domain adaptation,
we propose a novel online weighted adaptation regularization for regression
(OwARR) algorithm to reduce the amount of subject-specific calibration data,
and also a source domain selection (SDS) approach to save about half of the
computational cost of OwARR. Using a simulated driving dataset with 15
subjects, we show that OwARR and OwARR-SDS can achieve significantly smaller
estimation errors than several other approaches. We also provide comprehensive
analyses on the robustness of OwARR and OwARR-SDS.
