Sometimes they can yield valuable information. Artifacts are not always a hindrance to EEG interpretation. The proposed approach achieved average mutual information (MI) as 2.9684 ± 0.7045 on real EEG data. Muscle artifact from the temporal, frontal, and occipital areas is so frequently encountered that, for some portions of the tracing, the challenge of interpretation is to read through these artifacts to see the true EEG activity. 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. Next, the identified EEG signal is decomposed into wavelet coefficients and corrected through a modified NLM filter. 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. The main objective of this paper is to remove muscle artifacts without distorting the information contained in the EEG. 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.

Muscle artifact meaning