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Low Complexity Automatic Stationary Wavelet Transform for Elimination of Eye Blinks from EEG

1
Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, 51423 Kaunas, Lithuania
2
Biomedical Engineering Institute, Kaunas University of Technology, 51423 Kaunas, Lithuania
3
UAB Ortho Baltic, 51124 Kaunas, Lithuania
4
Department of Biomedical Engineering, Dezful Branch, Islamic Azad University, Dezful 6461645169, Iran
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Brain Sci. 2019, 9(12), 352; https://doi.org/10.3390/brainsci9120352
Received: 18 November 2019 / Accepted: 27 November 2019 / Published: 2 December 2019
The electroencephalogram signal (EEG) often suffers from various artifacts and noises that have physiological and non-physiological origins. Among these artifacts, eye blink, due to its amplitude is considered to have the most influence on EEG analysis. In this paper, a low complexity approach based on Stationary Wavelet Transform (SWT) and skewness is proposed to remove eye blink artifacts from EEG signals. The proposed method is compared against Automatic Wavelet Independent Components Analysis (AWICA) and Enhanced AWICA. Normalized Root Mean Square Error (NRMSE), Peak Signal-to-Noise Ratio (PSNR), and correlation coefficient ( ρ ) between filtered and pure EEG signals are utilized to quantify artifact removal performance. The proposed approach shows smaller NRMSE, larger PSNR, and larger correlation coefficient values compared to the other methods. Furthermore, the speed of execution of the proposed method is considerably faster than other methods, which makes it more suitable for real-time processing. View Full-Text
Keywords: electroencephalography; eye blink; SWT; skewness electroencephalography; eye blink; SWT; skewness
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Shahbakhti, M.; Maugeon, M.; Beiramvand, M.; Marozas, V. Low Complexity Automatic Stationary Wavelet Transform for Elimination of Eye Blinks from EEG. Brain Sci. 2019, 9, 352.

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