Abstract
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.
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Acknowledgments
This research is financially supported by the National Natural Science Foundation of China (No. 61401401, 61172086, 61402421, U1204607), the China Postdoctoral Science Foundation (No. 2014M561998) and the young teachers special Research Foundation Project of Zhengzhou University (No. 1411318029).
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Wang, F., Wang, Z., Li, R. (2015). Improved EEG Analysis Models and Methods Using Blind Source Separation. In: Deng, Z., Li, H. (eds) Proceedings of the 2015 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46466-3_38
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DOI: https://doi.org/10.1007/978-3-662-46466-3_38
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