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Brain-Computer Interface (BCI) system provides a pathway between humans andthe outside world by analyzing brain signals which contain potential neuralinformation. Electroencephalography (EEG) is one of most commonly used brainsignals and EEG recognition is an important part of BCI system. Recently,convolutional neural networks (ConvNet) in deep learning are becoming the newcutting edge tools to tackle the problem of EEG recognition. However, trainingan effective deep learning model requires a big number of data, which limitsthe application of EEG datasets with a small number of samples. In order tosolve the issue of data insufficiency in deep learning for EEG decoding, wepropose a novel data augmentation method that add perturbations to amplitudesof EEG signals after transform them to frequency domain. In experiments, weexplore the performance of signal recognition with the state-of-the-art modelsbefore and after data augmentation on BCI Competition IV dataset 2a and ourlocal dataset. The results show that our data augmentation technique canimprove the accuracy of EEG recognition effectively.

An amplitudes-perturbation data augmentation method in convolutional neural networks for EEG decoding