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Related Experiment Video

Updated: Sep 26, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with

Souvik Phadikar¹, Nidul Sinha¹, Rajdeep Ghosh²

¹Department of Electrical Engineering, National Institute of Technology Silchar, Silchar 788010, Assam, India.

Sensors (Basel, Switzerland)

|April 23, 2022

Summary

This study introduces a new method to remove muscle artifacts from electroencephalogram (EEG) signals using wavelet packet decomposition and a modified non-local means algorithm. The approach effectively cleans EEG data for better brain-computer interface and medical diagnoses.

Keywords:

brain–computer interface (BCI)electroencephalogram (EEG)electromyogram (EMG)modified non-local means filter (NLM)muscle artifacts

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Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

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Published on: June 23, 2023

Area of Science:

Biomedical Engineering
Signal Processing
Neuroscience

Background:

Muscle artifacts contaminate electroencephalogram (EEG) signals, compromising accuracy in brain-computer interface (BCI) systems and medical diagnoses.
Effective artifact removal is crucial for reliable EEG interpretation.

Purpose of the Study:

To develop a novel, multi-stage method for removing muscle artifacts from EEG signals without losing essential information.
To enhance the quality of EEG data for improved BCI performance and diagnostic accuracy.

Main Methods:

A hybrid approach combining wavelet packet decomposition (WPD) with a modified non-local means (NLM) algorithm for EEG signal denoising.
Utilized a pre-trained classifier for artifact identification, followed by WPD, NLM filtering, and inverse WPD for reconstruction.
Employed two meta-heuristic algorithms for optimizing filter parameters, ensuring an automated and efficient denoising process.

Main Results:

The proposed method demonstrated superior performance in removing muscle artifacts from EEG signals.
Achieved an average mutual information (MI) of 2.9684 ± 0.7045 on real EEG data, indicating high-quality signal reconstruction.
Outperformed existing denoising techniques in terms of reconstruction quality and automation.

Conclusions:

The novel multi-stage EEG denoising method effectively removes muscle artifacts while preserving crucial signal information.
The system's fully automatic nature and superior performance make it a valuable tool for BCI and clinical applications.
This approach offers a significant advancement in EEG signal processing for both research and diagnostic purposes.