Unsupervised Neural Decoding to Predict Dexterous Multi-Finger Flexion and Extension Forces

Accurate control over individual fingers of robotic hands is essential for the progression of human-robot interactions. Accurate prediction of finger forces becomes imperative in this context. The state-of-the-art neural decoders can extract neural signals from surface electromyogram (sEMG) signals. However, these decoders require labeled data for decoder training, which is challenging to obtain in cases such as limb loss and limits decoder generalizability. In our study, we extracted motoneuron firing information by decomposing high-density sEMG signals from both finger flexor and extensor muscles. We assigned each neuron a probability, reflecting its association with the targeted fingers, based on its temporal firing rate distribution. We then employed a probability thresholding and weighting strategy to select and prioritize neurons for finger force predictions. Our results revealed that the unsupervised neural decoder significantly outperformed both the supervised neural decoder and sEMG-amplitude approaches (${{R}^2}$: 0.74 ± 0.028 vs. 0.70 ± 0.028 vs. 0.63 ± 0.031, root mean square error: 6.74 ± 0.60% vs. 8.41 ± 0.56% vs. 10.33 ± 0.59% of maximum force), thereby offering a promising and practical solution for accurate force controls. Our results also demonstrated high computational efficiency (96.26 ± 24.16 ms), viable for real-time implementations. The outcomes offer an unsupervised decoder with simplified data requirements for decoder training. The decoder boasts enhanced functionality and adaptability in predicting finger flexion and extension forces. In addition, our approach holds promise for broader applications in scenarios where force measurement proves challenging.