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A fully automatic ocular artifact suppression from EEG data using higher order statistics: Improved performance by wavelet analysis
2010
Medical Engineering and Physics
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
doi:10.1016/j.medengphy.2010.04.010
pmid:20466582
fatcat:u3zc2qyrabazphptjxrlp2qkve