Impact of Encapsulation Tissue Growth on Selective Recording in Nerve Cuff Electrodes: A Simulation Study

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 systems. 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. We investigated the performance of a CNN-based selective recording approach in the presence of encapsulation tissue, a common immune response to the implantation of a neural interface. This factor was simulated using anatomically accurate computational models of a rat sciatic nerve and nerve cuff electrode. Performance over time was examined in three conditions: training the CNN at baseline only, supervised retraining with explicitly labeled data at periodic intervals, and a semi-supervised self-learning approach. The periodic recalibration approach demonstrated the best results, with an average F1-score of 0.96 ± 0.04, 0.89 ± 0.08, and 0.80 ± 0.08 for SNRs of -5 dB, -10 dB, and -15 dB, respectively, across all time points. Thus, the periodic recalibration approach may be an effective solution to compensate for changes in signal recordings seen over time as a result of encapsulation tissue. The self-learning approach, in which a network is retrained periodically using predicted labels, generally showed degradation in classification performance over time, even as the frequency of training was increased, attributed to an eventual accumulation of mislabeled training data.