Compensation Strategies for Bioelectric Signal Changes in Chronic Selective Nerve Cuff Recordings: A Simulation Study

Abstract

Peripheral nerve interfaces (PNIs) allow us to extract motor, sensory, and autonomic information from the nervous system and use it as control signals in neuroprosthetic and neuromodulation applications. Recent efforts have aimed to improve the recording selectivity of PNIs, including by using spatiotemporal patterns from multi-contact nerve cuff electrodes as input to a convolutional neural network (CNN). Before such a methodology can be translated to humans, its performance in chronic implantation scenarios must be evaluated. In this simulation study, approaches were evaluated for maintaining selective recording performance in the presence of two chronic implantation challenges: the growth of encapsulation tissue and rotation of the nerve cuff electrode. Performance over time was examined in three conditions: training the CNN at baseline only, supervised re-training with explicitly labeled data at periodic intervals, and a semi-supervised self-learning approach. This study demonstrated that a selective recording algorithm trained at baseline will likely fail over time due to changes in signal characteristics resulting from the chronic challenges. Results further showed that periodically recalibrating the selective recording algorithm could maintain its performance over time, and that a self-learning approach has the potential to reduce the frequency of recalibration.

Enhancing the selective electrical activation of human vagal nerve fibers: a comparative computational modeling study with validation in a rat sciatic model

“…Furthermore, while computational simulation of single nerve fibers can provide useful predictions of nerve activation [21,34,49], future FEM models could be expanded into a multi-fiber and or multi-fascicular model to investigate activation of specific peripheral targets. Chronic implantation can be simulated via the modeling of progressive growth of encapsulation tissue [50]. Stimulation parameters can also be expanded to include variations in pulse width, waveform, and electrode polarities.…”

Closed-Loop Control of Functional Electrical Stimulation Using a Selectively Recording and Bidirectional Nerve Cuff Interface

“…Studies may be performed to optimize the recalibration process so that it can be quickly and easily performed at home by the user. Sammut et al simulated natural changes in nerve cuff signals due to connective tissue growth and device rotation, demonstrating that a semi-supervised self learning approach may be used to reduce the frequency of CNN re-training [29]. To further reduce recalibration frequency, machine learning methods may be explored to generate new synthetic data based on predicted trends in signal characteristics to be evaluated using the semi-supervised self learning approach.…”

Closed-loop Control of Functional Electrical Stimulation Using a Selectively Recording and Bidirectional Nerve Cuff Interface

“…Studies may be performed to optimize the recalibration process so that it can be performed at home by the user in a minimal amount of time with minimal turnaround time for an updated and usable CNN. Sammut et al simulated natural changes in nerve cuff signals due to connective tissue growth and device rotation, demonstrating that a semi-supervised self learning approach may be used to reduce the frequency of recalibration [30]. To further reduce recalibration frequency, machine learning methods may be explored to autogenerate new data based on predicted trends in signal characteristics to be evaluated using the semi-supervised self learning approach.…”