Influences of design aspects on recording functionality of cuff-type peripheral nerve interfaces

OBJECTIVE: High-quality recording of peripheral nerve signals is a critical requirement for implantable neural interfaces, yet the influence of contact configuration and electrical insulation on cuff electrode recording quality remains poorly understood. APPROACH: Using a physical nerve model in vitro, the transfer function of commercially available split-cylinder and spiral cuff electrodes was measured for systematically varied contact configurations, including contact size, spacing, and size ratio of cathode to anode, as well as degree of electrical insulation. To derive single-fiber and compound action potentials, the transfer function was convolved with modeled action currents, enabling direct comparison of recording quality across designs. MAIN RESULTS: Contact edge spacing was found to be more decisive than contact surface area for recording amplitude, regardless of contact segmentation. In quasi-tripolar configurations, common mode suppression additionally reduced sensitivity to geometric variations. Both the length of the longitudinal insulation and the integrity of the electrode seal were found to strongly influence recording amplitude, with complete removal of the seal reducing amplitude by approximately 50%, outweighing all geometric design parameters combined. SIGNIFICANCE: These findings establish electrical insulation integrity as the primary design criterion for cuff electrode recording performance, shifting focus away from contact geometry alone, and provide a practical framework for the development of next-generation peripheral nerve interfaces.

Authors

Institutions

Publication Details

Journal
Journal of Neural Engineering
Published
2026-09-07
DOI
https://doi.org/10.1088/1741-2552/aea36d
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Influences of design aspects on recording functionality of cuff-type peripheral nerve interfaces

Ronny Pfeifer, Martin Schüettler, Fabian Pfeiffer, Thomas C. Stieglitz et al.
Journal of Neural Engineering
Neuroscience and Neural Engineering
article

Influences of design aspects on recording functionality of cuff-type peripheral nerve interfaces

Ronny Pfeifer, Martin Schüettler, Fabian Pfeiffer, Thomas C. Stieglitz, Seline Alicia Mahler, Emma Müller, Vera Anna-Lea Oppelt
article en

Abstract

OBJECTIVE: High-quality recording of peripheral nerve signals is a critical requirement for implantable neural interfaces, yet the influence of contact configuration and electrical insulation on cuff electrode recording quality remains poorly understood. APPROACH: Using a physical nerve model in vitro, the transfer function of commercially available split-cylinder and spiral cuff electrodes was measured for systematically varied contact configurations, including contact size, spacing, and size ratio of cathode to anode, as well as degree of electrical insulation. To derive single-fiber and compound action potentials, the transfer function was convolved with modeled action currents, enabling direct comparison of recording quality across designs. MAIN RESULTS: Contact edge spacing was found to be more decisive than contact surface area for recording amplitude, regardless of contact segmentation. In quasi-tripolar configurations, common mode suppression additionally reduced sensitivity to geometric variations. Both the length of the longitudinal insulation and the integrity of the electrode seal were found to strongly influence recording amplitude, with complete removal of the seal reducing amplitude by approximately 50%, outweighing all geometric design parameters combined. SIGNIFICANCE: These findings establish electrical insulation integrity as the primary design criterion for cuff electrode recording performance, shifting focus away from contact geometry alone, and provide a practical framework for the development of next-generation peripheral nerve interfaces.

Journal of Neural Engineering
University of Freiburg (DE), Technische Universität Berlin (DE)
Openalex Percentile: Top 16%
Neuroscience and Neural Engineering
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.