Prediction of Individual Finger Forces Based on Decoded Motoneuron Activities.

Accurate prediction of motor output based on neural signals is critical in human-machine interactions. The objective was to evaluate the performance of predicting individual finger forces through an estimation of the descending neural drive to the spinal motoneuron pool. High-density surface electromyogram (EMG) signals of the extensor digitorum communis muscle were obtained, and were then decomposed into individual motor unit discharge events. The frequency of the composite discharge events at the population level was used to derive the descending neural drive, which was then used to predict the finger forces. The global EMG-based approach was used as a control condition. Compared with the EMG-based approach, the experimental results show that the neural-drive-based approach can better predict the individual finger forces with higher R2 values across different force levels and across different force trajectories (steady and varying forces). These findings indicate that the neural drive estimation based on motoneuron firing activities can be used as a reliable neural-machine interface signal involving individual fingers. However, real-time implementation of this approach is needed for future clinical translation.