TY - JOUR TI - Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review AU - He, Chao AU - Liu, Jialu AU - Zhu, Yuesheng AU - Du, Wencai T2 - Frontiers in Human Neuroscience AB - Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and 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, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development. DA - 2021/12/17/ PY - 2021 DO - 10.3389/fnhum.2021.765525 DP - USJ Library VL - 15 SP - 765525 J2 - Front. Hum. Neurosci. SN - 1662-5161 ST - Data Augmentation for Deep Neural Networks Model in EEG Classification Task UR - https://www.frontiersin.org/articles/10.3389/fnhum.2021.765525/full Y2 - 2022/04/28/11:36:26 ER -