FBLPF-ABOW: An Effective Method for Blink Artifact Removal in Single-Channel EEG Signal

• Published in: IEEE journal of biomedical and health informatics Vol. 27; no. 12; pp. 5722 - 5733
• Main Authors: Gao, Wenjia, Liu, Dan, Wang, Qisong, Zhao, Yongping, Sun, Jinwei
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive bi-orthogonal wavelet (ABOW); Adaptive filters; Algorithms; Biomedical signal processing; blink artifact signals; Blinking; Discrete Wavelet Transform; discrete wavelet transformation (DWT); Discrete wavelet transforms; EEG; Electrodes; Electroencephalography; Electroencephalography - methods; Finite impulse response filters; Humans; Low pass filters; Signal Processing, Computer-Assisted; single-channel electroencephalography (EEG) signals; Transforms; Wavelet Analysis; Wavelet transforms
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2023.3314197

Objective: The latest development in low-cost single-channel Electroencephalography (EEG) devices is gaining widespread attention because it reduces hardware complexity. Discrete wavelet transform (DWT) has been a popular solution to eliminate the blink artifacts in EEG signals. However, the existing DWT-based methods share the same wavelet function among subjects, which ignores the individual difference. To remedy this deficiency, this article proposes a novel approach to eliminate the blink artifacts in single-channel EEG signals. Methods: Firstly, the forward-backward low-pass filter (FBLPF) and a fixed-length window are used to detect blink artifact intervals. Secondly, the adaptive bi-orthogonal wavelet (ABOW) is constructed based on the most representative blink signal. Thirdly, these detected signals are filtered by ABOW-DWT. The DWT's decomposition depth is automatically chosen by a similarity-based method. Results: Compared to eight state-of-the-art methods, experiments on semi-simulated and real EEG signals demonstrate the proposed method's superiority in removing the blink artifacts with less neural information loss. Significance: To filter the blink artifacts in single-channel EEG signals, the innovative idea of constructing an adaptive wavelet function based on the signal characteristics rather than using the conventional wavelet is proposed for the first time.