Peripheral Nerve Signals for Neural Control

Abstract

Neural signals recorded in functional sections of the nervous system where voluntary movement has been retained can be used to control prosthetic devices to assist patients to regain lost function. The number of signals recorded to control these devices can be increased by using a single multi-contact electrode placed over a multi-fasciculated peripheral nerve. Recordings made using these electrodes can then be separated using blind signal separation (BSS) methods to recover individual fascicular neural activity. In this study, we first determine the feasibility of recording selectively using nerve cuff electrodes. A flat nerve interface electrode was applied to the hypoglossal nerve to record signals from 11 contacts. Individual fascicles were activated to determine the ability of the electrode to detect signals from the various fascicles. The results show that fascicular signals can be distinguished in the presence of physiological noise amplitudes. We then investigate the feasibility of recovering the individual fascicular signals. We implement a blind source separation (BSS) algorithm BSS using independent component analysis (ICA) and investigate the effects of the number of contacts used and electrode layout on separation. Peripheral neural signals were simulated using a finite element model of the hypoglossal nerve of adult beagle dogs with a multi-contact cuff electrode placed around it. FastICA was then used to separate simulated neural signals. The separated and processed neural signals were then compared to the original source signals in the fascicles using correlation coefficient (CC) calculations. For n = 50 trials, the CC values obtained were all higher than 0.9 indicating that BSS can be used to recover linearly mixed independent fascicular neural signals recorded using a multi-contact cuff electrode. However, the order of the signals is lost during the recovery process. In order to solve the ambiguity of the recovered signals a novel method was designed and tested. In this method, a template of the demixing matrix is built during a training period. Each new estimated signals is compared to the template and the columns rearranged to restore the correct order of the recovered signals. These results suggest that it is possible to obtain multiple control signals from fasciculated peripheral nerves.