Generative Adversarial Networks for Electroencephalogram Signal Analysis: A Mini Review

被引：1

作者：

Wang, Junkongshuai ^{[1
]}

Mu, Wei ^{[1
]}

Wang, Aiping ^{[1
]}

Wang, Lu ^{[1
]}

Han, Jiaguan ^{[1
]}

Wang, Pengchao ^{[1
]}

Niu, Lan ^{[2
]}

Bin, Jianxiong ^{[2
]}

Zhang, Lihua ^{[1
,2
]}

Kang, Xiaoyang ^{[1
,2
,3
,4
]}

机构：

[1] Fudan Univ, Inst Meta Med,Acad Engn & Technol,Lab Neural Inte, Minist Educ,Inst AI & Robot,State Key Lab Med Neu, MOE Frontiers Ctr Brain Sci,Engn Res Ctr AI & Rob, Shanghai, Peoples R China

[2] Ji Hua Lab, Foshan, Guangdong, Peoples R China

[3] Fudan Univ, Yiwu Res Inst, Chengbei Rd, Yiwu City 322000, Zhejiang, Peoples R China

[4] Zhejiang Lab, Res Ctr Intelligent Sensing, Hangzhou 311100, Peoples R China

来源：

2023 11TH INTERNATIONAL WINTER CONFERENCE ON BRAIN-COMPUTER INTERFACE, BCI | 2023年

基金：

中国国家自然科学基金; 国家重点研发计划;

关键词：

Generative adversarial network (GAN); braincomputer interface (BCI); electroencephalography (EEG); data augmentation;

D O I：

10.1109/BCI57258.2023.10078666

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页数：6

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