ABSTRACT
Electrical stimulation of peripheral nerves via implanted electrodes has been shown to be a promising approach to restore sensation, movement, and autonomic functions across a wide range of illnesses and injuries. While in principle computational models of neuromodulation can allow the exploration of large parameter spaces and the automatic optimization of stimulation devices and strategies, their high time complexity hinders their use on a large scale. We recently proposed the use of machine learning-based surrogate models to estimate the activation of nerve fibers under electrical stimulation, producing a considerable speed-up with respect to biophysically accurate models of fiber excitation while retaining good predictivity. Here, we characterize the performance of four frequently employed machine learning algorithms and provide an illustrative example of their ability to generalize to unseen stimulation protocols, stimulating sites, and nerve sections. We then discuss how the ability to generalize to such scenarios is relevant to different optimization protocols, paving the way for the automatic optimization of neuromodulation applications.
Bioelectromagnetics, Volume 46, Issue 1, January 2025.
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ABSTRACT
Electrical stimulation of peripheral nerves via implanted electrodes has been shown to be a promising approach to restore sensation, movement, and autonomic functions across a wide range of illnesses and injuries. While in principle computational models of neuromodulation can allow the exploration of large parameter spaces and the automatic optimization of stimulation devices and strategies, their high time complexity hinders their use on a large scale. We recently proposed the use of machine learning-based surrogate models to estimate the activation of nerve fibers under electrical stimulation, producing a considerable speed-up with respect to biophysically accurate models of fiber excitation while retaining good predictivity. Here, we characterize the performance of four frequently employed machine learning algorithms and provide an illustrative example of their ability to generalize to unseen stimulation protocols, stimulating sites, and nerve sections. We then discuss how the ability to generalize to such scenarios is relevant to different optimization protocols, paving the way for the automatic optimization of neuromodulation applications.
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