  Data augmentation approaches are widely explored for the enhancement of
decoding electroencephalogram signals. In subject-independent brain-computer
interface system, domain adaption and generalization are utilized to shift
source subjects' data distribution to match the target subject as an
augmentation. However, previous works either introduce noises (e.g., by noise
addition or generation with random noises) or modify target data, thus, cannot
well depict the target data distribution and hinder further analysis. In this
paper, we propose a target-centered subject transfer framework as a data
augmentation approach. A subset of source data is first constructed to maximize
the source-target relevance. Then, the generative model is applied to transfer
the data to target domain. The proposed framework enriches the explainability
of target domain by adding extra real data, instead of noises. It shows
superior performance compared with other data augmentation methods. Extensive
experiments are conducted to verify the effectiveness and robustness of our
approach as a prosperous tool for further research.
