Removing EOG Artifacts from the Resting State EEG Signal of Methamphetamine Addicts by ICA Algorithms

被引：0

作者：

Zhan, Gege ^{[1
]}

Su, Haolong ^{[1
]}

Wang, Pengchao ^{[1
]}

Niu, Lan ^{[2
]}

Bin, Jianxiong ^{[2
]}

Mu, Wei ^{[1
]}

Zhang, Xueze ^{[1
]}

Jiang, Haifeng ^{[3
,4
]}

Zhang, Lihua ^{[1
,2
]}

Kang, Xiaoyang ^{[1
,2
,5
,6
]}

机构：

[1] Fudan Univ, Acad Engn & Technol,Shanghai Engn Res Ctr AI & Ro, Inst Metamed,MOE Frontiers Ctr Brain Sci,Minist E, Inst AI & Robot,State Key Lab Med Neurobiol,Lab N, Shanghai, Peoples R China

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

[3] Shanghai Jiao Tong Univ, Shanghai Mental Ctr, Sch Med, Shanghai, Peoples R China

[4] Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai, Peoples R China

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

[6] 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：

EEG; Artifact removal; Independent component analysis (ICA); EOG;

D O I：

10.1109/BCI57258.2023.10078556

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

EEG signal contains a wealth of information about brain activity, but the recording process is inevitably contaminated by EOG artifacts. An effective method to remove EOG artifacts can provide a guarantee for subsequent EEG analysis. In this paper, we compare the performance of four ICA algorithms in removing EOG artifacts from EEG signals of methamphetamine addicts. From the perspective of time domain and power spectral density, all the four algorithms can effectively remove the EOG artifacts without obvious difference. In terms of PSNR, MI and processing speed, FastICA algorithm can achieve higher processing speed and reconstruct signals better than the other three algorithms.

引用

页数：5

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