Sparse Uncorrelated Cross-Domain Feature Extraction for Signal Classification in Brain-Computer Interfaces

In this paper, we present a novel dimensionality reduction method, called sparse uncorrelated cross-domain feature extraction (SUFE), for signal classification in brain-computer interfaces (BCIs). Considering the differences between the source and target distributions of signals from difl"erent subjects, we construct an optimization objective which aims to find a projection matrix to transform the original data in a high-dimensional space into a low-dimensional latent space. In the low-dimensional space, both the discrimination of different classes and transferability between the source and target domains are preserved. To make sure the minimum information redundancy, the extracted features are designed to be statistically uncorrelated. Then, by adding the l(1)-norm penalty, we incorporate sparsity into the uncorrelated transformation. In the experiments, we evaluate the method with multiple datasets, and compare with the state-of-the-art methods. The results show that the proposed approach has better performance and is suitable for cross-domain signal classification.