EEGDnet: Fusing non-local and local self-similarity for EEG signal denoising with transformer

doi:10.1016/j.compbiomed.2022.106248

	EEGDnet: Fusing non-local and local self-similarity for EEG signal denoising with transformer
	Pu, Xiaorong 1; Yi, Peng 2; Chen, Kecheng 4; Ma, Zhaoqi 2; Zhao, Di 3; Ren, Yazhou 1
	2022-12-01
发表期刊	COMPUTERS IN BIOLOGY AND MEDICINE
ISSN	0010-4825
卷号	151 页码:8
摘要	Electroencephalogram (EEG) has shown a useful approach to produce a brain-computer interface (BCI). One-dimensional (1-D) EEG signal is yet easily disturbed by certain artifacts (a.k.a. noise) due to the high temporal resolution. Thus, it is crucial to remove the noise in received EEG signal. Recently, deep learning-based EEG signal denoising approaches have achieved impressive performance compared with traditional ones. It is well known that the characteristics of self-similarity (including non-local and local ones) of data (e.g., natural images and time-domain signals) are widely leveraged for denoising. However, existing deep learning-based EEG signal denoising methods ignore either the non-local self-similarity (e.g., 1-D convolutional neural network) or local one (e.g., fully connected network and recurrent neural network). To address this issue, we propose a novel 1-D EEG signal denoising network with 2-D transformer, namely EEGDnet. Specifically, we comprehensively take into account the non-local and local self-similarity of EEG signal through the transformer module. By fusing non-local self-similarity in self-attention blocks and local self-similarity in feed forward blocks, the negative impact caused by noises and outliers can be reduced significantly. Extensive experiments show that, compared with other state-of-the-art models, EEGDnet achieves much better performance in terms of both quantitative and qualitative metrics. Specifically, EEGDnet can achieve 18% and 11% improvements in correlation coefficients when removing ocular artifacts and muscle artifacts, respectively.
关键词	Electroencephalography Artifact removal Transformer
DOI	10.1016/j.compbiomed.2022.106248
收录类别	SCI
语种	英语
资助项目	Open Foundation of Nuclear Medicine Laboratory of Mianyang Central Hospital[2021HYX017] ; Sichuan Science and Technology Program[2021YFS0172] ; Sichuan Science and Technology Program[2022YFS0047] ; Sichuan Science and Technology Program[2022YFS0055] ; Clinical Research Incubation Project, West China Hospital, Sichuan University[2021HXFH004] ; Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China[ZYGX2021YGLH022]
WOS研究方向	Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology
WOS类目	Biology ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology
WOS记录号	WOS:000900186300008
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：14[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/20131
专题	中国科学院计算技术研究所期刊论文
通讯作者	Chen, Kecheng; Ren, Yazhou
作者单位	1.Univ Elect Sci & Technol China UESTC, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China 2.Univ Elect Sci & Technol China UESTC, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100080, Peoples R China 4.City Univ Hong Kong, Dept Elect Engn, Hong Kong 999077, Peoples R China
推荐引用方式 GB/T 7714	Pu, Xiaorong,Yi, Peng,Chen, Kecheng,et al. EEGDnet: Fusing non-local and local self-similarity for EEG signal denoising with transformer[J]. COMPUTERS IN BIOLOGY AND MEDICINE,2022,151:8.
APA	Pu, Xiaorong,Yi, Peng,Chen, Kecheng,Ma, Zhaoqi,Zhao, Di,&Ren, Yazhou.(2022).EEGDnet: Fusing non-local and local self-similarity for EEG signal denoising with transformer.COMPUTERS IN BIOLOGY AND MEDICINE,151,8.
MLA	Pu, Xiaorong,et al."EEGDnet: Fusing non-local and local self-similarity for EEG signal denoising with transformer".COMPUTERS IN BIOLOGY AND MEDICINE 151(2022):8.