Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-heuristically Optimized Non-local Means Filter

TL;DR

This paper introduces a novel multi-stage method combining wavelet packet decomposition and a meta-heuristically optimized non-local means filter to automatically remove muscle artifacts from single-channel EEG signals, improving data quality for BCI and medical diagnosis.

Contribution

It presents the first combined use of wavelet decomposition with a meta-heuristic optimized non-local means filter for EEG artifact removal, enhancing denoising effectiveness.

Findings

Achieved an average mutual information of 2.9684 on real EEG data.

Outperformed recent denoising techniques in quality of reconstruction.

Validated on both simulated and real EEG datasets.

Abstract

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. The main objective of this paper is to remove muscle artifacts without distorting the information contained in the EEG. 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. At first, the artifact EEG signal is identified through a pre-trained classifier. Next, the identified EEG signal is decomposed into wavelet coefficients and corrected through a modified NLM filter. Finally, the artifact-free EEG is reconstructed from corrected wavelet coefficients through inverse WPD. To optimize the filter parameters, two meta-heuristic algorithms are used in this paper for the first time. The proposed system is first validated on simulated EEG data and then tested on real EEG data. The proposed approach achieved average mutual information (MI) as 2.9684 $\pm$ 0.7045 on real EEG data. The result reveals that the proposed system outperforms recently developed denoising techniques with higher average MI, which indicates that the proposed approach is better in terms of quality of reconstruction and is fully automatic.