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A flat-interface nerve electrode (FINE) is presented as a potential solution for using multifascicle nerve recordings as part of a 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 (FEM) 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≤SI≤1; ability of the electrode to distinguish two active sources). Computer simulations yielded a selective range (0.05≤SI≤0.76) that was 1) related to the interfiber distance and 2) used to predict the minimum interfiber distance (0.23 mm≤d≤1.42 mm) for selective recording at each SNR. The SI was further evaluated using recorded compound APs elicited from electrically activating the branches of the beagle hypoglossal nerve. For all experiments (n=7), the selectivity (SI=0.45±0.16) was within the range predicted by the FEM. This study suggests that the FINE can record the activity from a multifasciculated nerve and, more importantly, distinguish neural signals from pairs of fascicles at physiologic SNR.