Localization and Recovery of Peripheral Neural Sources With Beamforming Algorithms

The peripheral nervous system carries sensory and motor information that could be useful as command signals for function restoration in areas such as neural prosthetics and functional electrical stimulation (FES). Nerve cuff electrodes provide a robust and safe technique for recording nerve signals. However, a method to separate and recover signals from individual fascicles is necessary. Prior knowledge of the electrode geometry was used to develop an algorithm which assumes neither signal independence nor detailed knowledge of the nerve's geometry/conductivity, and is applicable to any wide-band near-field situation. When used to recover fascicular activities from simulated nerve cuff recordings in a realistic human femoral nerve model, this beamforming algorithm separates signals as close as 1.5 mm with cross-correlation coefficient, ${ R}>0.9$ (10% noise). Ten simultaneous signals could be recovered from individual fascicles with only a 20% decrease in $R$ compared to a single signal. At high noise levels (40%), sources were localized to $180\pm 170\ \mu{\rm m}$ in the 12 $\times$ 3 mm cuff. Localizing sources and using the resulting positions in the recovery algorithm yielded ${ R}=0.66\pm 0.10$ in 10% noise for five simultaneous muscle-activation signals from synergistic fascicles. These recovered signals should allow natural, robust, closed-loop control of multiple degree-of-freedom prosthetic devices and FES systems.