Target-centered Subject Transfer Framework for EEG Data Augmentation
This work addresses data scarcity and noise issues in EEG-based brain-computer interfaces, offering a more effective augmentation tool for researchers, though it appears incremental as it builds on existing domain adaptation and generative models.
The paper tackles the problem of data augmentation for EEG signal decoding in subject-independent brain-computer interfaces by proposing a target-centered subject transfer framework that enriches target domain data with real data instead of noise, showing superior performance compared to other methods.
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.