Data-driven Data Augmentation for Motor Imagery Brain-Computer Interface

When utilizing deep learning (DL) for the brain-computer interface (BCI), it suffers from degradation of classification performance due to the small quantity of electroencephalography (EEG) training data. Typically, artifacts such as blinking and a falling-off in user's attention caused by a long experiment period have a great effect on the small quantity and the low quality of the EEG training data. To address this problem, in this work, we introduce a novel classification method based on data augmentation method and DL framework for motor imagery (MI) EEGs classification. The proposed framework generates input EEG images with distinct MI features from a small amount of EEG training data. Experimental results using a public BCI dataset show that the proposed method outperforms the predecessors which use a simple DL model.