Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada.
KITE, Toronto Rehab, University Health Network, Toronto, ON M5G 2A2, Canada.
Sensors (Basel). 2021 Jan 12;21(2):506. doi: 10.3390/s21020506.
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.
周围神经接口 (PNI) 使我们能够从神经系统中提取运动、感觉和自主信息，并将其用作神经假肢和神经调节应用中的控制信号。最近的研究旨在提高 PNI 的记录选择性，包括使用多接触神经袖套电极的时空模式作为卷积神经网络 (CNN) 的输入。在将这种方法转化为人类之前，必须在慢性植入场景中评估其性能。在这项模拟研究中，评估了几种方法来维持选择性记录性能，以应对两种慢性植入挑战：封装组织的生长和神经袖套电极的旋转。在三种情况下检查了随时间的性能：仅在基线时训练 CNN、定期使用显式标记数据进行监督重新训练，以及半监督自学习方法。这项研究表明，由于慢性挑战导致信号特征发生变化，基线训练的选择性记录算法可能会随着时间的推移而失效。结果还表明，定期重新校准选择性记录算法可以随着时间的推移保持其性能，并且自学习方法有可能降低重新校准的频率。
