The proposed approach achieved average mutual information (MI) as 2.9684 ± 0.7045 on real EEG data.
The proposed system is first validated on simulated EEG data and then tested on real EEG data. To optimize the filter parameters, two meta-heuristic algorithms are used in this paper for the first time. Finally, the artifact-free EEG is reconstructed from corrected wavelet coefficients through inverse WPD. muscle artifacts, that cause confusion in the analysis and interpretation process (Song and Sepulveda, 2018, Ren et al., 2019, Lopez-Larraz et al., 2018, Nathan and Contreras-Vidal, 2016). Next, the identified EEG signal is decomposed into wavelet coefficients and corrected through a modified NLM filter. Introduction Electroencephalogram (EEG) signal is often contaminated by non-cerebral artifacts, e.g. At first, the artifact EEG signal is identified through a pre-trained classifier. A novel multi-stage EEG denoising method is proposed for the first time in which wavelet packet decomposition (WPD) is combined with a modified non-local means (NLM) algorithm. Amyogenic (muscle) artifact consists of brief potentials that may occur. The main objective of this paper is to remove muscle artifacts without distorting the information contained in the EEG. For the successful interpretation of an abnormal EEG one must first understand. Electroencephalogram (EEG) signals may get easily contaminated by muscle artifacts, which may lead to wrong interpretation in the brain–computer interface (BCI) system as well as in various medical diagnoses.
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