A toolchain to simulate and investigate selective stimulation strategies for FES

When contracting a muscle using NFES (Neural Functional Electrical Stimulation), the stimulus always activates the axons of greater diameter first. Also selective activation of given fascicle inside a nerve is not possible with classical cuff electrode as the recruitment is performed uniformly around the nerve. These limits lead to poorly selective muscle recruitment, inducing fatigue and possible pain. To overcome this, selective stimulation strategies can be used. We propose a toolchain to investigate, simulate and tune selective stimulation strategies. It consists of a conduction volume model to compute the electric field generated in the nerve by a cuff electrode surrounding it; an axon model to predict the effect of the field on the nerve fibre - the generation, propagation and possible block of action potentials; and an interface script that links the two models and generates the code of the input function for the nerve fibre model. We present some simulation results to illustrate the possibilities of the toolchain to simulate such strategies. Ongoing experimental validations are also discussed. They will enable us to tune the model and may lead to further improvements.