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

文献类型：期刊论文

作者Pu, Xiaorong4; Yi, Peng3; Chen, Kecheng1; Ma, Zhaoqi3; Zhao, Di2; Ren, Yazhou4
刊名COMPUTERS IN BIOLOGY AND MEDICINE
出版日期2022-12-01
卷号151页码:8
关键词Electroencephalography Artifact removal Transformer
ISSN号0010-4825
DOI10.1016/j.compbiomed.2022.106248
英文摘要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.
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
语种英语
WOS记录号WOS:000900186300008
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.204/handle/2XEOYT63/20130]  
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.City Univ Hong Kong, Dept Elect Engn, Hong Kong 999077, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing 100080, Peoples R China
3.Univ Elect Sci & Technol China UESTC, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China
4.Univ Elect Sci & Technol China UESTC, Sch Comp Sci & Engn, Chengdu 611731, 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.

入库方式： OAI收割

来源：计算技术研究所

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