On the intersubject generalization ability in extracting kinematic information from afferent nervous signals

In the recent past, many efforts have been carried out in order to evaluate the feasibility of implementing closed-loop controlled neuroprostheses based on the processing of sensory electroneurographic (ENG) signals. The success of these techniques mostly relies on the development of processing algorithms capable of extracting the necessary kinematic information from these signals. Soft-computing algorithms can be very useful when dealing with the complexity of the neuromuscular system because of their generalization ability and model-free structure. In this paper, these techniques were used to extract angular position information from the ENG signals recorded from muscle afferents in animal model using cuff electrodes. Specifically, a genetic algorithm-based dynamic nonsingleton fuzzy logic system (named GA-DNSFLS) was developed and tested on different types of angular trajectories (characterized by small or large angular excursions). In particular, two different Takagi-Sugeno-Kang (TSK)-like structures were used in the consequent part of the neuro-fuzzy model in order to verify which one could improve the generalization abilities (intrasubject and intersubject). The results showed that the GA-DNSFLS was able to reconstruct the trajectories giving interesting results in terms of correlation between the actual and the predicted trajectories for small excursion movements during intrasubject and intersubject tests. Particularly, one of the TSK models showed better results in terms of intersubject generalization. The simulations conducted with the large excursion movements led in some cases to interesting results but further experiments are necessary in order to analyze this point more in deep.