Uncorrelated transferable feature extraction for signal classification in brain-computer interfaces

This paper presents a novel dimensionality reduction method, called uncorrelated transferable feature extraction (UTFE), for signal classification in brain-computer interfaces (BCIs). Considering the difference between the source and target distributions of signals from different subjects, we construct an optimization objective that finds a projection matrix to transform the original data in a high-dimensional space into a low-dimensional latent space and that guarantees both the discrimination of different classes and transferability between the source and target domains. In the low-dimensional latent space, the model constructed in the source domain can generalize well to the target domain. Additionally, the extracted features are statistically uncorrelated, which ensure the minimum informative redundancy in the latent space. In the experiments, we evaluate the method with data from nine BCI subjects, and compare with the state-of-the-art methods. The results demonstrate that our method has better performance and is suitable for signal classification in BCIs.