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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.

Embedding watermark into H.264/AVC streams directly can reduce computational complexity compared to encoder-based watermarking algorithms. However, it would cause intra error propagation and decrease the video quality. To improve the video quality and reduce the computational complexity, an improved compensation scheme is developed in this paper. In this scheme, only some of the integer transform (IT) coefficients are processed as opposed to process all the IT-coefficients as seen in other compensation methods. This approach helps to reduce the computation complexity of this compensation scheme. The simulation results have shown that our proposed method has lower computational complexity compared to other algorithms.Index Terms-H.264/AVC, integer transform, intra error propagation, watermarking.

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