Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review - He, Chao; Liu, Jialu; Zhu, Yuesheng; Du, Wencai (Frontiers in Human Neuroscience, 2021)
Classification of electroencephalogram (EEG) is a key tool to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). Due to the nonlinearity and non-stationarity of EEGs, it was difficult to obtain the expected performance for current algorithms. With the development of artificial intelligence, various advanced algorithms were proposed for EEG classification. Among them, deep neural networks (DNNs) gradually become the most attractive strategy due to their end-to-end structure and the powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, researchers have proposed a promising approach to improve the performance of the decoding model based on data augmentation (DA). In this manuscript, we conduct a systematic literature review for related works published in nearly five years to investigate the different DA strategies for EEG classification based on DNNs. More precisely, the review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are used to improve the DNN models, and what kind of accuracy can be obtained. The review summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research.