Improved EEG Analysis Models and Methods Using Blind Source Separation
Noninvasive assessing the physiological changes occurring inside the human brain is a challenging problem in biomedical engineering.These variations can be modeled as biomedical source signals that can be measured by several types of noninvasive brain imaging techniques such as electroencephalography(EEG).In this paper,after the perspective of linear blind source separation(BSS)model and characteristics of EEG are presented,the general and detailed definition of BSS model for EEG data analysis is given.Then based on the spatial structure and temporal or spectral information of the EEG signals,some state-of-the-art BSS techniques that can be used for analyzing EEG recordings are reviewed.A novel algorithm combining both high-order statistics and second-order statistics to achieve BSS for EEG is constructed.The paper concludes by discussing the influence of BSS for EEG research.
Blind source separation Electroencephalogram High-order statistics(HOS) Second-order statistics(SOS) Independent component analysis(ICA)
Fasong Wang Zhongyong Wang Rui Li
School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China
国际会议
The 2015 Chinese Intelligent Automation Conference(2015中国智能自动化会议)
福州
英文
381-387
2015-05-08（万方平台首次上网日期，不代表论文的发表时间）