This article presents a hybrid wavelet-based algorithm to suppress the ocular artifacts from electroencephalography (EEG) signals. The hybrid wavelet transform (HWT) method is designed by the combination of discrete wavelet decomposition and wavelet packet transform. The artifact suppression is performed by the selection of sub-bands obtained by HWT. Fractional Gaussian noise (fGn) is used as the reference signal to select the sub-bands containing the artifacts. The multichannel EEG signal is decomposed HWT into a finite set of sub-bands. The energies of the sub-bands are compared to that of the fGn to the desired sub-band signals. The EEG signal is reconstructed by the selected sub-bands consisting of EEG. The experiments are conducted for both simulated and real EEG signals to study the performance of the proposed algorithm. The results are compared with recently developed algorithms of artifact suppression. It is found that the proposed method performs better than the methods compared in terms of performance metrics and computational cost.

本文提出了一种基于小波的混合算法，可从脑电图（EEG）信号中抑制眼部伪影。混合小波变换（HWT）方法是结合离散小波分解和小波包变换设计的。通过选择由HWT获得的子带来执行伪像抑制。分数高斯噪声（fGn）用作参考信号，以选择包含伪像的子带。将多通道EEG信号分解为一组有限的子带。将子带的能量与fGn的能量进行比较，以得到所需的子带信号。通过选择的包括EEG的子带来重建EEG信号。对模拟和真实的脑电信号进行了实验，以研究所提出算法的性能。将结果与最新开发的伪影抑制算法进行比较。发现所提出的方法在性能指标和计算成本方面比所比较的方法更好。