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BioMed Research International
Volume 2015 (2015), Article ID 720450, 8 pages
Research Article

Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information

China National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450002, China

Received 4 December 2014; Revised 13 March 2015; Accepted 18 March 2015

Academic Editor: Tsair-Fwu Lee

Copyright © 2015 Chi Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA), wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition.