Online EMG Artifacts Removal from EEG Based on Blind Source Separation

被引：4
|
作者：
Gao, Junfeng [1 ]
Yang, Yong [2 ,3 ]
Lin, Pan [4 ]
Wang, Pei [1 ]
机构：
[1] Xi An Jiao Tong Univ, Res Inst Biomed Engn, Xian, Peoples R China
[2] Jiangxi Univ Finance & Econ, Sch Informat Technol, Nanchang, Jiangxi, Peoples R China
[3] Univ Elect Sci & Technol China, Sch Life Sci & Technol, Chengdu, Peoples R China
[4] Univ Trent, Funct NeuroImaging Lab Ctr Mind Brain Sci, Trento, Italy
来源：
2010 2ND INTERNATIONAL ASIA CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS (CAR 2010), VOL 1 | 2010年
基金：
中国国家自然科学基金;
关键词：
canonical correlation analysis (CCA); Electromyography (EMG) artifacts; independent component analysis (ICA); INDEPENDENT COMPONENT ANALYSIS; RECORDINGS;
D O I：
10.1109/CAR.2010.5456848
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Electromyography (EMG) artifacts are the main and serious contaminated sources to the electroencephalogram (EEG) signals. In this paper, a fully automated EMG removal technique based on canonical correlation analysis (CCA) method is presented. CCA method was proved more suitable to reconstruct the EMG-free EEG data than independent component analysis (ICA) methods in the study. Specially, a number of contaminated and clean EEG data were analyzed in order to decide a reasonable correlation threshold, by which this method can remove successfully not only the light EMG artifacts but also heavy EMG artifacts from the EEG data in real-time application with the little distortion of not only the underlying ictal activity signal but the EOG artifacts.
引用
收藏
页码：28 / 31
页数：4
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