ArticleSensors (Basel, Switzerland)2022
Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-Heuristically Optimized Non-Local Means Filter.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
- Improved SSVEP Classification Through EEG Artifact Reduction Using Auxiliary Sensors.Sensors (Basel, Switzerland) · 2026Article
- Deep Hybrid CNN-BiLSTM-Attention Model for EEG Classification Using Wavelet Features.Annals of neurosciences · 2026Article
- The Role of Intraoperative Neurophysiological Monitoring in Intracranial Cavernous Malformation Surgery: A Narrative Review.Iranian journal of medical sciences · 2025Review
- Instantiating the onEEGwaveLAD Framework for Real-Time Muscle Artefact Identification and Mitigation in EEG Signals.Sensors (Basel, Switzerland) · 2025Article
- Hybrid DWT NLM method with NOA optimization for ECG signal denoising.Scientific reports · 2025Article
- P300 ERP System Utilizing Wireless Visual Stimulus Presentation Devices.Sensors (Basel, Switzerland) · 2025Article
- Analysis of frequency domain features for the classification of evoked emotions using EEG signals.Experimental brain research · 2025Article
- Interindividual variability and individual stability of pain- and touch-related neuronal gamma oscillations.Journal of neurophysiology · 2023Article
- Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends.Bioengineering (Basel, Switzerland) · 2023Review
- Continual Learning of a Transformer-Based Deep Learning Classifier Using an Initial Model from Action Observation EEG Data to Online Motor Imagery Classification.Bioengineering (Basel, Switzerland) · 2023Article
- EEG Emotion Recognition Applied to the Effect Analysis of Music on Emotion Changes in Psychological Healthcare.International journal of environmental research and public health · 2022Article
- Exploiting the Cone of Influence for Improving the Performance of Wavelet Transform-Based Models for ERP/EEG Classification.Brain sciences · 2022Article
- EEG Microstate Features as an Automatic Recognition Model of High-Density Epileptic EEG Using Support Vector Machine.Brain sciences · 2022Article
- Explainable Artificial Intelligence Model for Stroke Prediction Using EEG Signal.Sensors (Basel, Switzerland) · 2022Article
- Few-Electrode EEG from the Wearable Devices Using Domain Adaptation for Depression Detection.Biosensors · 2022Article
- EEG Network Analysis in Epilepsy with Generalized Tonic-Clonic Seizures Alone.Brain sciences · 2022Article
- Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals.Brain sciences · 2022Article
- Deep Unsupervised Domain Adaptation with Time Series Sensor Data: A Survey.Sensors (Basel, Switzerland) · 2022Review
- Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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 ± 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.
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Registered trials
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