Sub-band selection approach to artifact suppression from electroencephalography signal using hybrid wavelet transform

• Published in: International journal of advanced robotic systems Vol. 18; no. 1
• Main Authors: Ferdous, Jannatul, Ali, Sujan, Hamid, Ekramul, Molla, Khademul Islam
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-01-2021; Sage Publications Ltd; SAGE Publishing
• Subjects: Algorithms; Decomposition; Electroencephalography; Performance measurement; Random noise; Reference signals; Wavelet analysis; Wavelet transforms; Artifact suppression; EEG; human–robot interaction; wavelet transform; neurorobotics; sub-band decomposition; fractional Gaussian noise
• ISSN: 1729-8806; 1729-8814
• DOI: 10.1177/1729881421992269

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.