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

Mohammad Shahbakhti, Maxime Maugeon, Matin Beiramvand, Vaidotas Marozas

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Abstract

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.
Original languageEnglish
Article number352
JournalBrain Sciences
Volume9
Issue number12
DOIs
Publication statusPublished - 2 Dec 2019
Externally publishedYes
Publication typeA1 Journal article-refereed

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