IEEE Transactions on Neural Systems and Rehabilitation Engineering | 2019

Intraoperative Responses May Predict Chronic Performance of Composite Flat Interface Nerve Electrodes on Human Femoral Nerves

 
 
 
 
 
 
 

Abstract


Peripheral nerve cuff electrodes (NCEs) in motor system neuroprostheses can generate strong muscle contractions and enhance surgical efficiency by accessing multiple muscles from a single proximal location. Predicting chronic performance of high contact density NCEs based on intraoperative observations would facilitate implantation at locations that maximize selective recruitment, immediate connection of optimal contacts to implanted pulse generators (IPGs) with limited output channels, and initiation of postoperative rehabilitation as soon as possible after surgery. However, the stability of NCE intraoperative recruitment to predict chronic performance has not been documented. Here we report the first-in-human application of a specific NCE, the composite flat interface nerve electrode (C-FINE), at a new and anatomically challenging location on the femoral nerve close to the inguinal ligaments. EMG and moment recruitment curves were recorded for each of the 8 contacts in 2 C-FINE intraoperatively, perioperatively, and chronically for 6 months. Intraoperative measurements predicted chronic outcomes for 87.5% of contacts with 14/16 recruiting the same muscles at 6 months as intraoperatively. In both 8-contact C-FINEs, 3 contacts elicited hip flexion and 5 selectively generated knee extension, 3 of which activated independent motor unit populations each sufficient to support standing. Recruitment order stabilized in less than 3 weeks and did not change thereafter. While confirmation of these results will be required with future studies and implant locations, this suggests that remobilization and stimulated exercise may be initiated 3 weeks after surgery with little risk of altering performance.

Volume 27
Pages 2317-2327
DOI 10.1109/TNSRE.2019.2951079
Language English
Journal IEEE Transactions on Neural Systems and Rehabilitation Engineering

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