Knowledge Transfer for Reducing Calibration Time in Brain-Computer Interfacing

Reducing calibration time while maintaining good classification accuracy has been one of the most challenging problems in electroencephalography (EEG) -based brain-computer interfaces (BCIs) research during the last years. Most of machine learning approaches that have been attempted to address this issue are based on knowledge transfer between different BCIs users. Assuming that there is a common underlying data generating process, they try to learn a subject-independent classification model from multiple users in order to classify data of future users. In this paper, we propose a novel approach that allows inter-subjects classification of EEG signals without relying on the strong assumptions considered in previous work. It consists of learning a prediction model of a new BCI user through an ensemble of classifiers where base classifiers are trained on data from other users separately and weighted according to the performance of the ensemble on few labeled data of the new user. Evaluation on real EEG data showed that our approach allows achieving good classification accuracy when the size of calibration set is small.