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Open AccessArticle

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

by 1,2, 2 and 1,2,3,4,*
1
Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada
2
KITE, Toronto Rehab, University Health Network, Toronto, ON M5G 2A2, Canada
3
Edward S Rogers Sr Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M4G 3V9, Canada
4
Rehabilitation Sciences Institute, University of Toronto, Toronto, ON M5S 2E4, Canada
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(2), 506; https://doi.org/10.3390/s21020506
Received: 23 November 2020 / Revised: 5 January 2021 / Accepted: 8 January 2021 / Published: 12 January 2021
(This article belongs to the Special Issue Implantable Systems for Biomedical Applications)
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. View Full-Text
Keywords: neural interfaces; neural recording; peripheral nerve; nerve cuff electrode; chronic implantation; selective recording; machine learning; self-learning; encapsulation tissue neural interfaces; neural recording; peripheral nerve; nerve cuff electrode; chronic implantation; selective recording; machine learning; self-learning; encapsulation tissue
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MDPI and ACS Style

Sammut, S.; Koh, R.G.L.; Zariffa, J. Compensation Strategies for Bioelectric Signal Changes in Chronic Selective Nerve Cuff Recordings: A Simulation Study. Sensors 2021, 21, 506.

AMA Style

Sammut S, Koh RGL, Zariffa J. Compensation Strategies for Bioelectric Signal Changes in Chronic Selective Nerve Cuff Recordings: A Simulation Study. Sensors. 2021; 21(2):506.

Chicago/Turabian Style

Sammut, Stephen; Koh, Ryan G.L.; Zariffa, José. 2021. "Compensation Strategies for Bioelectric Signal Changes in Chronic Selective Nerve Cuff Recordings: A Simulation Study" Sensors 21, no. 2: 506.

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