title: The recording properties of a multi-contact nerve electrode as predicted by a finite element model of the canine hypoglossal nerve.
abstract: Most functional electrical stimulation (FES) systems rely only on unidirectional (i.e., efferent) activation of the target organ to yield therapeutic outcomes. For applications involving multi-fasciculated nerves, however, artificial sensors have exhibited limited results. As such, the flat-interface-nerve-electrode (FINE) is presented as a means of obtaining an effective closed-loop control system. To investigate the ability of this electrode to achieve selective recordings at physiological signal-to-noise ratio (SNR), a finite element model (JFEM) of a beagle hypoglossal nerve with an implanted FINE was constructed. Action potentials (AP) were generated at various SNR levels and the performance of the electrode was assessed with a selectivity index (0 < or = SI < or = 1; ability of the electrode to distinguish two active sources). Computer simulations yielded a selective range (0.05 < or = SI < or = 0.76) that was (1) related to the inter-fiber distance and (2) used to predict the minimum inter-fiber distance (0.23 mm < or = d < or = 1.42 mm) required for selective recording. The results of this study suggest that the FINE can record neural activity from a multi-fasciculated nerve and, more importantly, distinguish neural activity from pairs of fascicles at physiologic SNR.
authors.affiliation: Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
authors.forename: P B
authors.initials: PB
authors.lastname: Yoo
authors.forename: D M
authors.initials: DM
authors.lastname: Durand