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Open AccessArticle

Improved EOG Artifact Removal Using Wavelet Enhanced Independent Component Analysis

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Department of Electrical Engineering and Information Systems, Faculty of Information Technology, University of Pannonia, Egyetem u.10, 8200 Veszprém, Hungary
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Department of Scientific Computing, Faculty of Computers and Informatics, Benha University, Fareed Nada, Benha 13511, Egypt
*
Author to whom correspondence should be addressed.
Brain Sci. 2019, 9(12), 355; https://doi.org/10.3390/brainsci9120355
Received: 20 September 2019 / Revised: 21 November 2019 / Accepted: 2 December 2019 / Published: 4 December 2019
(This article belongs to the Special Issue Handling Uncertainty in EEG Signal Pattern Recognition)
Electroencephalography (EEG) signals are frequently contaminated with unwanted electrooculographic (EOG) artifacts. Blinks and eye movements generate large amplitude peaks that corrupt EEG measurements. Independent component analysis (ICA) has been used extensively in manual and automatic methods to remove artifacts. By decomposing the signals into neural and artifactual components and artifact components can be eliminated before signal reconstruction. Unfortunately, removing entire components may result in losing important neural information present in the component and eventually may distort the spectral characteristics of the reconstructed signals. An alternative approach is to correct artifacts within the independent components instead of rejecting the entire component, for which wavelet transform based decomposition methods have been used with good results. An improved, fully automatic wavelet-based component correction method is presented for EOG artifact removal that corrects EOG components selectively, i.e., within EOG activity regions only, leaving other parts of the component untouched. In addition, the method does not rely on reference EOG channels. The results show that the proposed method outperforms other component rejection and wavelet-based EOG removal methods in its accuracy both in the time and the spectral domain. The proposed new method represents an important step towards the development of accurate, reliable and automatic EOG artifact removal methods. View Full-Text
Keywords: EEG; EOG artifacts removal; independent component analysis; discrete wavelet transform (DWT) EEG; EOG artifacts removal; independent component analysis; discrete wavelet transform (DWT)
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Issa, M.F.; Juhasz, Z. Improved EOG Artifact Removal Using Wavelet Enhanced Independent Component Analysis. Brain Sci. 2019, 9, 355.

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