Multi-source manifold feature transfer learning with domain selection for brain-computer interfaces

Abstract

Transfer learning uses the knowledge in source domains to improve the learning performance in the target domain, which is useful in electroencephalogram (EEG) based brain-computer interfaces (BCIs) with small training datasets. However, the existing transfer learning methods for EEG based BCI mainly consider the knowledge transfer from single-to-single (STS) domain or simply merge different source domains into a bigger one. In this paper, we propose a multi-source manifold feature transfer learning (MMFT) framework to transfer multi-source knowledge for EEG signals classification. MMFT minimizes marginal probability distribution on the Riemannian manifold using Riemannian alignment and Grassmann manifold feature learning, then transfers the manifold features with a conditional probability distribution adaptation in the structural risk minimization (SRM) function. Based on MMFT, w-MMFT is proposed to tackle the class imbalance issue for SRM, and the label similarity analysis (LSA) is proposed to select source domains for MMFT, forming a new LSA-MMFT framework. Experimental results on six datasets demonstrate that the proposed MMFT has achieved superior performance in classification accuracy and computational efficiency compared to state-of-the-art methods. The LSA-MMFT can get more stable performance than two other domain selection methods.