Diffusion models-based motor imagery EEG sample augmentation via mixup strategy

Abstract

Deep representation learning has been widely explored for decoding motor imagery electroencephalogram (MI-EEG) to build EEG-tailored brain-computer interfaces. Due to the labor-intensive and time-consuming recording to MI-EEG, recently BCIs were suffered from sample scarcity, especially for the limitation of sample diversity. To solve this issue, we proposed a novel sample augmentation method, Diffusion models-based EEG Sample Augmentation method via Mixup strategy (DESAM), to solve sample scarcity and improve sample diversity for both multivariate time-series and time–frequency images forms. To reduce the characteristics conflicts brought by the augmented samples, a mixup strategy with data weighting was proposed for both forms. To validate the efficacy of sample augmentation by DESAM, we conducted comparative experiments on three publicly available MI-EEG datasets, and reported the average decoding accuracies and Kappa values on augmented samples by various deep learning models. For the BCI Competition IV datasets 2a, 2b, and 1, the two forms of DESAM sample augmentations achieved improvements for both accuracies and Kappa values. Ablation studies have demonstrated the necessity and significance of our DESAM method, and it makes a possible manner to solve sample scarcity issue of MI-EEG.