Real-time isometric finger extension force estimation based on motor unit discharge information

Area of Science:

• Neuroscience
• Biomedical Engineering
• Motor Control

Background:

• Accurate estimation of muscle force is crucial for understanding motor control and developing advanced neural interfaces.
• Conventional methods using electromyogram (EMG) amplitude can be unreliable during prolonged contractions.
• Motor unit (MU) discharge information offers a potentially more direct measure of neural drive.

Purpose of the Study:

• To develop and evaluate a real-time method for estimating isometric finger extension force using MU discharge information.
• To compare the performance of the novel neural-drive method against a conventional EMG amplitude-based method.

Main Methods:

• A real-time EMG decomposition technique utilizing Fast Independent Component Analysis (FastICA) was employed to extract MU discharge events from high-density (HD) EMG recordings.
• A neural-drive method was established, estimating force based on MU pool discharge probability and a firing rate-force model.
• Simulated and experimental isometric finger extension data were used to assess decomposition accuracy and force estimation performance over time.

Main Results:

• Real-time MU decomposition accuracy was 86%, stable over time, compared to 94% offline accuracy.
• The neural-drive method demonstrated significantly lower root mean square error (RMSE) for force estimation compared to the EMG-amplitude method across all fingers.
• The neural-drive method's RMSE remained stable for up to 230 seconds, whereas the EMG-amplitude method's RMSE increased progressively.

Conclusions:

• The neural-drive method provides a more accurate and robust approach to real-time finger force estimation compared to conventional EMG amplitude methods, especially during sustained muscle activity.
• This technique holds promise for enhancing the reliability of neural interface systems by leveraging detailed motor unit pool activity.