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Updated: Jul 16, 2026
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Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
Published on: July 26, 2013
Real time ECG artifact removal for myoelectric prosthesis control
Ping Zhou1, Blair Lock, Todd A Kuiken
1Neural Engineering Center for Artificial Limbs, Rehabilitation Institute of Chicago, Chicago, IL, USA.
Physiological Measurement
|March 31, 2007
Summary
This study presents efficient methods for removing electrocardiogram (ECG) artifacts from electromyogram (EMG) signals in real time. These techniques are crucial for improving the performance of myoelectric prosthesis control systems.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Rehabilitation Technology
Background:
- Electrocardiogram (ECG) artifacts significantly contaminate electromyogram (EMG) signals from torso muscles, posing a challenge for myoelectric prosthesis control.
- Real-time removal of these artifacts is critical for the speed and efficiency required in clinical applications.
Purpose of the Study:
- To investigate and evaluate simple, fast methods for real-time removal of ECG artifacts from EMG signals.
- To optimize these methods for effective myoelectric prosthesis control.
Main Methods:
- Investigated digital high-pass filtering, quantifying the effects of cutoff frequency and filter order.
- Developed an adaptive spike-clipping approach for dynamic ECG artifact detection and suppression.
- Combined high-pass filtering and adaptive spike-clipping for comprehensive artifact removal.
Main Results:
- Quantified EMG signal distortion and ECG artifact suppression for each method.
- Determined optimal parameter assignments for each method based on performance across various ECG/EMG ratios.
- Demonstrated the effectiveness of the combined approach in suppressing ECG artifacts.
Conclusions:
- Efficient real-time ECG artifact removal methods were identified and optimized for myoelectric prosthesis control.
- The developed techniques offer practical solutions for enhancing the reliability of EMG-based prosthetic devices.
- Optimal parameter selection is key to maximizing performance in diverse clinical scenarios.
