Title: Automated blink artefact removal from EEG using variational mode decomposition and singular spectrum analysis
Authors: Poonam Sheoran; J.S. Saini
Addresses: Department of Biomedical Engineering, Deenbandhu Chhotu Ram University of Sc. and Tech., Murthal, Sonepat, Haryana, India ' Department of Electrical Engineering, Deenbandhu Chhotu Ram University of Sc. and Tech., Murthal, Sonepat, Haryana, India
Abstract: Blink artefacts are the major source of noise while acquiring electroencephalogram (EEG) data for analysis. To design an efficient method for blink artefact removal is essential for conducting any sort of analysis using EEG. In this paper, a novel automated eye blink artefact removal method based on variational mode decomposition (VMD) and singular spectrum analysis (SSA) is presented. The noisy EEG signals are first separated into uncorrelated components using canonical correlation analysis (CCA) and then variational mode decomposition is performed for multiresolution analysis. The decomposed components (modes) are assessed through their singular values for finding the distribution of noise using singular spectrum analysis. Phase space reconstruction (PSR) is also used to differentiate the clean modes and noisy modes. The applicability of the proposed approach is examined through statistical measures like signal to noise ratio (SNR), correlation coefficient and root mean square error (RMSE). The results indicate the efficacy of the approach in artefact removal without manual intervention as compared to the state-of-the-art technologies. The proposed method automatically identified and removed the noisy fraction of signal, yielding the requisite neural information without any manual intervention.
Keywords: artefact removal; variational mode decomposition; VMD; canonical correlation analysis; CCA; phase space reconstruction; PSR; singular spectrum analysis; SSA.
International Journal of Biomedical Engineering and Technology, 2021 Vol.36 No.1, pp.64 - 78
Received: 21 Feb 2018
Accepted: 07 Jun 2018
Published online: 23 Jun 2021 *