CutCat: An augmentation method for EEG classification

The non-invasive electroencephalogram (EEG) signals enable humans to communicate with devices and have control over them, this process requires precise classification and identification of those signals. The recent revolution of deep learning has empowered both feature extraction and classification in a joint manner of different data types. However, deep learning is a data learning approach that demands a large number of training samples. Whilst, the EEG research field lacks a large amount of data which restricts the use of deep learning within this field. This paper proposes a novel augmentation method for enlarging EEG datasets. Our CutCat augmentation method generates trials from inter- and intra-subjects and trials. The method relies on cutting a specific period from an EEG trial and concatenating it with a period from another trial from the same subject or different subjects. The method has been tested on shallow and deep convolutional neural networks (CNN) for the classification of motor imagery (MI) EEG data. Two input formulation types images and time-series have been used as input to the neural networks. Short-time Fourier transform (STFT) is used for generating training images from the time-series signals. The experimental results demonstrate that the proposed augmentation method is a promising strategy for handling the classification of small-scale datasets. Classification results on two EEG datasets show advancement in comparison with the results of state-of-the-art researches.