Abstract Combination of the wavelet transform and independent component analysis (ICA) was employed for blind source separation (BSS) of multichannel electroencephalogram (EEG). After denoising the original signals by discrete wavelet transform, high frequency components of some noises and artifacts were removed from the original signals. The denoised signals were reconstructed again for the purpose of ICA, such that the drawback that ICA cannot distinguish noises from source signals can be overcome effectively. The practical processing results showed that this method is an effective way to BSS of multichannel EEG. The method is actually a combination of wavelet transform with adaptive neural network, so it is also useful for BBS of other complex signals.
Received: 05 August 2004
Revised: 09 May 2005
Accepted manuscript online:
Fund: Project supported by the National Natural Science Foundation of China (Grant No 10234070) and
by the Science Foundation of Educational Commission of Fujian Province of China (Grant No JAO04238).
Cite this article:
You Rong-Yi (游荣义), Chen Zhong (陈忠) Blind source separation of multichannel electroencephalogram based on wavelet transform and ICA 2005 Chinese Physics 14 2176
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