IEEE J Biomed Health Inform. 2023 Dec;27(12):5722-5733. doi: 10.1109/JBHI.2023.3314197. Epub 2023 Dec 5.
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.
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.
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.
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.
低成本单通道脑电图（EEG）设备的最新发展受到广泛关注，因为它降低了硬件复杂度。离散小波变换（DWT）一直是消除 EEG 信号中的眨眼伪迹的常用解决方案。然而，现有的基于 DWT 的方法在受试者之间共享相同的小波函数，忽略了个体差异。为了弥补这一不足，本文提出了一种消除单通道 EEG 信号中眨眼伪迹的新方法。
首先，使用前后低通滤波器（FBLPF）和固定长度窗口检测眨眼伪迹间隔。其次，基于最具代表性的眨眼信号构建自适应双正交小波（ABOW）。然后，这些检测到的信号通过 ABOW-DWT 进行滤波。DWT 的分解深度通过基于相似性的方法自动选择。
与八种最先进的方法相比，半仿真和真实 EEG 信号的实验表明，该方法在去除眨眼伪迹的同时，神经信息损失较少，具有优越性。
为了滤除单通道 EEG 信号中的眨眼伪迹，首次提出了一种基于信号特征而不是使用传统小波构建自适应小波函数的创新思想。
