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

Objective: A reliable neural-machine interface offers the possibility of controlling advanced robotic hands with high dexterity. The objective of this study was to develop a decoding method to estimate flexion and extension forces of individual fingers concurrently. Methods: First, motor unit (MU) firing information was identified through surface electromyogram (EMG) decomposition, and the MUs were further categorized into different pools for the flexion and extension of individual fingers via a refinement procedure. MU firing rate at the populational level was calculated, and the individual finger forces were then estimated via a bivariate linear regression model (neural-drive method). Conventional EMG amplitude-based method was used as a comparison. Results: Our results showed that the neural-drive method had a significantly better performance (lower estimation error and higher correlation) compared with the conventional method. Conclusion: Our approach provides a reliable neural decoding method for dexterous finger movements. Significance: Further exploration of our method can potentially provide a robust neural-machine interface for intuitive control of robotic hands.