  Brain-computer interface (BCI) is a communication system between humans and
computers reflecting human intention without using a physical control device.
Since deep learning is robust in extracting features from data, research on
decoding electroencephalograms by applying deep learning has progressed in the
BCI domain. However, the application of deep learning in the BCI domain has
issues with a lack of data and overconfidence. To solve these issues, we
proposed a novel data augmentation method, CropCat. CropCat consists of two
versions, CropCat-spatial and CropCat-temporal. We designed our method by
concatenating the cropped data after cropping the data, which have different
labels in spatial and temporal axes. In addition, we adjusted the label based
on the ratio of cropped length. As a result, the generated data from our
proposed method assisted in revising the ambiguous decision boundary into
apparent caused by a lack of data. Due to the effectiveness of the proposed
method, the performance of the four EEG signal decoding models is improved in
two motor imagery public datasets compared to when the proposed method is not
applied. Hence, we demonstrate that generated data by CropCat smooths the
feature distribution of EEG signals when training the model.
