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Updated: Nov 7, 2025

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

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Published on: July 26, 2013

Hybrid algorithm for multi artifact removal from single channel EEG.

Sayedu Khasim Noorbasha¹, Gnanou Florence Sudha¹

¹Department of Electronics and Communication Engineering, Pondicherry Engineering College, Puducherry-605014, India.

Biomedical Physics & Engineering Express

|April 30, 2021

Summary

This study introduces a novel method combining Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) with Generalized Moreau Envelope Total Variation (GMETV) to effectively remove multiple artifacts from electroencephalogram (EEG) signals, improving diagnostic accuracy.

Keywords:

EEG Electrocardiogram Electrooculogram GMETV ICA Motion artifact

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Cortical Source Analysis of High-Density EEG Recordings in Children

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Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Area of Science:

Biomedical Engineering
Signal Processing
Neuroscience

Background:

Ambulatory Electroencephalogram (EEG) signals are frequently contaminated by artifacts such as electrooculogram (EOG), motion artifacts (MA), and electrocardiogram (ECG).
These artifacts significantly reduce the accuracy of EEG analysis and can lead to misdiagnoses.
Existing methods for artifact removal, especially for multiple artifacts in single-channel EEG, are limited.

Purpose of the Study:

To develop and evaluate a novel technique for simultaneously removing multiple artifacts from single-channel EEG signals.
To improve the accuracy and reliability of EEG data for clinical diagnosis and research.

Main Methods:

A combined approach using Singular Spectrum Analysis (SSA) for signal decomposition and Independent Component Analysis (ICA) for source separation.
Integration of Generalized Moreau Envelope Total Variation (GMETV) to accurately estimate and remove residual artifacts from independent components (ICs).
Subtraction of estimated artifacts from artifact-laden ICs and reintegration of the residual EEG signal into the clean ICs.

Main Results:

The proposed SSA-ICA-GMETV technique demonstrated superior performance in removing combined artifacts compared to existing methods.
Achieved a 12.02% and 7.22% reduction in Relative Root Mean Square Error (RRMSE) compared to SSA-ICA and SSA-ICA-thresholding, respectively.
Increased the Correlation Coefficient (CC) by 21.48% and 8.25% compared to SSA-ICA and SSA-ICA-thresholding, respectively, indicating better signal preservation.

Conclusions:

The synergistic combination of SSA, ICA, and GMETV offers an effective solution for simultaneous multi-artifact removal in single-channel EEG.
This advanced denoising method enhances EEG signal quality, paving the way for more accurate diagnostic interpretations.
The proposed technique holds significant potential for improving the clinical utility of ambulatory EEG monitoring.