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Pre-training, personalization, and self-calibration: all a neural network-based myoelectric decoder needs.

Chenfei Ma¹, Xinyu Jiang¹, Kianoush Nazarpour¹

¹School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom.

Frontiers in Neurorobotics

|August 12, 2025

Summary

This study introduces a novel three-stage training method for myoelectric control systems. The adaptive workflow improves and maintains electromyographic signal decoding performance over time for users.

Keywords:

adaptation deep learning myoelectric control neural network transfer learning

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Published on: November 6, 2015

Area of Science:

Biomedical Engineering
Neuroscience
Artificial Intelligence

Background:

Myoelectric control systems translate electromyographic signals (EMG) into movement intentions for prosthetics and robotics.
High variability and temporal changes in EMG signals challenge system generalization, personalization, and adaptation.
Deep neural networks require extensive user-specific data, limiting real-world application due to performance degradation.

Purpose of the Study:

To develop an adaptive workflow for neural network training in myoelectric control.
To improve and maintain decoding performance of electromyographic signals over extended periods.
To address the limitations of current systems in handling EMG signal variability and user-specific patterns.

Main Methods:

Proposed an innovative three-stage neural network training scheme.
Implemented an adaptive workflow to progressively enhance network performance.
Evaluated the system on 28 subjects over a 2-day period.

Main Results:

Demonstrated significant improvement and maintenance of network performance across subjects and time.
Validated the necessity and effectiveness of each stage within the proposed training framework.
Showcased the potential for robust and adaptive myoelectric control systems.

Conclusions:

The proposed three-stage training scheme effectively enhances adaptive myoelectric control.
The adaptive workflow is crucial for maintaining performance despite EMG signal variability.
This approach offers a promising solution for personalized and long-term use of EMG-based interfaces.