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Vol. 157, Issue 10, October 2013, pp. 253-256




Research of EEG Signal Feature Exaction Technology Based on the Blind Signal Separation Algorithm
Jiang Haitao, Li Suiyuan

Jiaozuo Teachers College, Jiaozuo, 454001, China
E-mail: jzchaonan@yahoo.com.cn


Received: 27 July 2013   /Accepted: 25 September 2013   /Published: 31 October 2013

Digital Sensors and Sensor Sysstems


Abstract: EEG signals processing is an important auxiliary tools in clinically analysis of brain disease. But EEG signals is affected by many factors with the characters of the signal is weak and noise is strong, the frequency range is low and the randomicity is strong, etc. It belongs to the unstable signal and using traditional Fourier transform can hard to carry out the de-noising or signal character value extracting, etc. And because of the wavelet analysis multi-resolution characteristics, it is widely applied to the EEG signals separation, but in no prior knowledge, wavelet analysis is difficult to judge the decomposition layer, thus it unable to realize signal separation. This paper based on the ICA model, used the natural gradient separation algorithm to separate the EEG signal with noise, and obtained the better separation results, through compared with the wavelet analysis separation results, the error result by using the natural gradient blind separation algorithm is not more than 1 Hz.


Keywords: Blind signal separation, EEG signal, Wavelet analysis, Natural gradient, ICA.


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