Journal
MEDICAL ENGINEERING & PHYSICS
Volume 32, Issue 7, Pages 720-729Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.medengphy.2010.04.010
Keywords
Electroencephalogram; Ocular artifact; Independent component analysis; Wavelet analysis
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Funding
- Iran Neural Technology Center, Iran University of Science and Technology
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Contamination of electroencephalographic (EEG) recordings with different kinds of artifacts is the main obstacle to the analysis of EEG data. Independent component analysis (ICA) is now a widely accepted tool for detection of artifacts in EEG data. One major challenge to artifact removal using ICA is the identification of the artifactual components. Although several strategies were proposed for automatically detecting the artifactual component during past several years, there is still little consensus on the criteria for automatic rejection of undesired components. In this paper we present a new identification procedure based on an efficient combination of independent component analysis (ICA), mutual information, and wavelet analysis for fully automatic ocular artifact suppression. The method does not require any offline training or determining the threshold levels for different markers. The results show that the proposed method could significantly enhance the ocular artifact detection and suppression. The results on 3105 4-s EEG epochs indicate that the artifact components can be identified with an accuracy of 97.8%, a sensitivity of 96.9%, and a specificity of 98.6%. (C) 2010 IPEM. Published by Elsevier Ltd. All rights reserved.
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