Riemannian geometry-based transfer learning for reducing training time in c-VEP BCIs

Abstract

One of the main problems that a brain-computer interface (BCI) face is that a training stage is required for acquiring training data to calibrate its classification model just before every use. Transfer learning is a promising method for addressing the problem. In this paper, we propose a Riemannian geometry-based transfer learning algorithm for code modulated visual evoked potential (c-VEP)-based BCIs, which can effectively reduce the calibration time without sacrificing the classification accuracy. The algorithm includes the main procedures of log-Euclidean data alignment (LEDA), super-trial construction, covariance matrix estimation, training accuracy-based subject selection (TSS) and minimum distance to mean classification. Among them, the LEDA reduces the difference in data distribution between subjects, whereas the TSS promotes the similarity between a target subject and the source subjects. The resulting performance of transfer learning is improved significantly. Sixteen subjects participated in a c-VEP BCI experiment and the recorded data were used in offline analysis. Leave-one subject-out (LOSO) cross-validation was used to evaluate the proposed algorithm on the data set. The results showed that the algorithm achieved much higher classification accuracy than the subject-specific (baseline) algorithm with the same number of training trials. Equivalently, the algorithm reduces the training time of the BCI at the same performance level and thus facilitates its application in real world.