  Brain-Computer Interface (BCI) system provides a pathway between humans and
the outside world by analyzing brain signals which contain potential neural
information. Electroencephalography (EEG) is one of most commonly used brain
signals and EEG recognition is an important part of BCI system. Recently,
convolutional neural networks (ConvNet) in deep learning are becoming the new
cutting edge tools to tackle the problem of EEG recognition. However, training
an effective deep learning model requires a big number of data, which limits
the application of EEG datasets with a small number of samples. In order to
solve the issue of data insufficiency in deep learning for EEG decoding, we
propose a novel data augmentation method that add perturbations to amplitudes
of EEG signals after transform them to frequency domain. In experiments, we
explore the performance of signal recognition with the state-of-the-art models
before and after data augmentation on BCI Competition IV dataset 2a and our
local dataset. The results show that our data augmentation technique can
improve the accuracy of EEG recognition effectively.
