Generative Adversarial Networks for Electroencephalogram Signal Analysis: A Mini Review
Junkongshuai Wang, Wei Mu, Aiping Wang, Lu Wang, Jiaguan Han, Pengchao Wang, Lan Niu, Jianxiong Bin, Lihua Zhang, Xiaoyang Kang
Abstract
Brain-computer interface (BCI) technology based on electroencephalography (EEG) signals is growing rapidly and attracting widespread attention. However, due to the EEG acquisition methods, the quality and quantity of EEG signals are not able to be guaranteed. To alleviate the problems caused by the lack of data, in this paper, we introduce the applications of EEG signals using generative adversarial networks (GANs) which have shown great performance in image data augmentation and other time series data and then discuss their performance.
Topics & Concepts
ElectroencephalographyComputer scienceGenerative grammarAdversarial systemBrain–computer interfaceSIGNAL (programming language)Artificial intelligenceGenerative adversarial networkSpeech recognitionPattern recognition (psychology)Machine learningImage (mathematics)PsychologyNeuroscienceProgramming languageEEG and Brain-Computer InterfacesBlind Source Separation TechniquesNeural dynamics and brain function