A New Nonlinear Autoregressive Exogenous (NARX)-Based Intra-Spinal Stimulation Approach to Decode Brain Electrical Activity for Restoration of Leg Movement in Spinally-Injured Rabbits

This study aims at investigation of stimulation by using intra-spinal signals decoded from electrocorticography (ECoG) assessments to restore the movements of the leg in an animal model of spinal cord injury (SCI). The present work comprised of three steps. First, ECoG signals and the associated leg joint changes (hip, knee, and ankle) in sedated healthy rabbits were recorded in different trials. Second, an appropriate set of intra-spinal electric stimuli was discovered to restore natural leg movements, using the three leg joint movements under fuzzy-controlled strategy in spinally-injured rabbits on anesthesia condition. Third, a nonlinear autoregressive exogenous (NARX) neural network model was developed to produce appropriate intra-spinal stimulation developed from decoded ECoG information. The model was able to correlate the ECoG signal data to the intra-spinal stimulation data and finally, induced desired leg movements. In this study, leg movements were also developed from off-line ECoG signals (deciphered from rabbits that were not injured) as well as on-line ECoG data (extracted from the same rabbit after SCI induction). Based on our data, correlation coefficient was 0.74±0.15 and normalized root mean square error of brain-spine interface was 0.22±0.10. Together, we found that using NARX, appropriate information from ECoG recordings can be extracted and used for generation of proper intra-spinal electric stimulations for restoration of natural leg movements lost due to SCI.