Concurrent Estimation of Finger Flexion and Extension Forces Using Motoneuron Discharge Information

Area of Science:

• Neuroscience
• Biomedical Engineering
• Robotics

Background:

• Neural-machine interfaces (NMIs) are crucial for controlling advanced robotic prosthetics.
• Dexterous control of individual finger movements remains a significant challenge in NMI development.

Purpose of the Study:

• To develop and validate a novel decoding method for estimating individual finger flexion and extension forces concurrently.
• To compare the performance of the new method against conventional electromyogram (EMG) amplitude-based techniques.

Main Methods:

• Surface electromyogram (EMG) decomposition was used to identify motor unit (MU) firing information.
• MUs were categorized into flexion/extension pools for individual fingers.
• A bivariate linear regression model (neural-drive method) estimated finger forces from MU firing rates.

Main Results:

• The neural-drive method demonstrated significantly lower estimation error compared to the conventional EMG amplitude-based method.
• Higher correlation between estimated and actual forces was achieved using the neural-drive method.
• The decoding method reliably estimated concurrent flexion and extension forces for individual fingers.

Conclusions:

• The developed neural-drive method offers a reliable approach for neural decoding of dexterous finger movements.
• This method provides a foundation for robust NMIs enabling intuitive control of robotic hands.