NDLI: A Method of Denoising Multi-channel EEG Signals Fast Based on PCA and DEBSS Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Dong Kang Luo Zhizeng
Copyright Year	2012
Abstract	A method of de-noising multi-channel EEG signals which combines the principle component analysis (PCA) with density estimation blind source separation (DEBSS) is proposed in this paper. Based on removing high frequency noise in wavelet analysis, the PCA algorithm is used to process the EEG signals to reduce the data dimension. Then, the DEBSS algorithm is adapted to separate the EEG signals which data dimension has been reduced. The main interference is identified and removed by using cross-correlation coefficient and related non-linear parameters to analyze the independent components. Finally, through reconstructing the remaining independent components, the EEG signals without main interference will be obtained. The experimental results show that this method can eliminate the main interference of multi-channel EEG signals rapidly and effectively, meanwhile, it is stable and has strong scalability.
Starting Page	322
Ending Page	326
File Size	273946
Page Count	5
File Format	PDF
ISBN	9781467306898
e-ISBN	9780769546476
DOI	10.1109/ICCSEE.2012.105
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	De-noising DEBSS algorithm Noise reduction Estimation Interference Electrocardiography Electroencephalography Blind source separation EEG signal Kernel Principal component analysis PCA
Content Type	Text
Resource Type	Article

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