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Removing electroencephalographic artifacts: comparison between ICA and PCA

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7 Author(s)
Tzyy-Ping Jung ; Comput. Neurobiol. Lab., Salk Inst., San Diego, CA, USA ; Humphries, C. ; Te-Won Lee ; Makeig, S.
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Pervasive electroencephalographic (EEG) artifacts associated with blinks, eye-movements, muscle noise, cardiac signals, and line noise poses a major challenge for EEG interpretation and analysis. Here, we propose a generally applicable method for removing a wide variety of artifacts from EEG records based on an extended version of the independent component analysis (ICA) algorithm for performing blind source separation on linear mixtures of independent source signals. Our results show that ICA can effectively separate and remove contamination from a wide variety of artifact sources in EEG records with results comparing favourably to those obtained using principal component analysis (PCA)

Published in:

Neural Networks for Signal Processing VIII, 1998. Proceedings of the 1998 IEEE Signal Processing Society Workshop

Date of Conference:

31 Aug-2 Sep 1998

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