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Related Experiment Video

Updated: Dec 25, 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

Improving EEG Muscle Artifact Removal With an EMG Array.

Juan Andrés Mucarquer¹, Pavel Prado², María-José Escobar¹

¹Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile.

IEEE Transactions on Instrumentation and Measurement

|March 25, 2020

Summary

This summary is machine-generated.

Adding electromyogram (EMG) data to ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) significantly improves electroencephalogram (EEG) artifact removal. Optimal performance is achieved with 16 EMG channels, offering a cost-effective enhancement for EEG analysis.

Keywords:

Adaptive filtering artifact removal blind-source-separation electroencephalogram (EEG)electromyogram (EMG)muscle artifacts

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Last Updated: Dec 25, 2025

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Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI

Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI

Published on: March 19, 2021

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Muscle activity during electroencephalogram (EEG) recording introduces artifacts, challenging data analysis.
Ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) is effective for EEG denoising but lacks artifact-specific information.
Existing methods do not leverage electromyogram (EMG) data, which directly captures muscle activity.

Purpose of the Study:

To enhance EEMD-CCA for EEG artifact removal by incorporating EMG data.
To evaluate the performance improvement gained by increasing the number of EMG channels.
To assess the impact of adaptive filtering using EMG as a reference for artifact removal.

Main Methods:

Extended EEMD-CCA to integrate an EMG array for artifact removal.
Employed adaptive filtering (recursive least squares) with the EMG array as a reference for CCA components.
Simulated noise scenarios using real and synthetic EEG and EMG signals with varying numbers of EMG channels.

Main Results:

A substantial performance improvement in artifact removal was observed as the number of EMG electrodes increased from 2 to 16.
Increasing EMG channels beyond 16 up to 128 did not yield significant additional performance gains.
The proposed method effectively ameliorates signal distortion caused by artifacts and denoising.

Conclusions:

Integrating EMG electrodes into the EEMD-CCA framework significantly enhances EEG artifact removal.
A small number of EMG channels (around 16) is sufficient to achieve substantial performance improvements.
This approach offers a computationally inexpensive yet highly effective enhancement for EEG signal processing.