Transfer Learning Based on Hybrid Riemannian and Euclidean Space Data Alignment and Subject Selection in Brain-Computer Interfaces

Abstract

Transfer learning is a promising approach for reducing training time in a brain-computer interface (BCI). However, how to effectively transfer data from previous users to a new user poses a huge challenge. This paper presents a novel transfer learning approach that combines data alignment and source subject selection for motor imagery (MI) based BCIs. The former is achieved by a reference matrix from the regularization of the two reference matrices estimated in Riemannian and Euclidean space respectively, whereas the latter is implemented by a modified sequential forward floating-point search algorithm. The aligned training data from chosen source subjects are used for creating a classification model based on either spatial covariance matrices in Riemannian space or common spatial pattern algorithm in Euclidean space. The proposed algorithms were evaluated on two MI based BCI data sets with different subjects and compared with existing transfer learning algorithms with sole data alignment or subject selection. The experimental results show that the hybrid-space data alignment methods for reducing the differences among subjects significantly outperform two single-space alignment methods, and the source subject selection method can substantially enhance the similarity between source subjects and the target subject. The combination of the two methods achieves superior classification performance compared to either one. The proposed algorithms will greatly facilitate the real-world applications of MI based BCIs.