By reconnecting the previously severed sense of touch, the field of neuroprosthetics has tremendous potential to substantially improve the lives of millions of amputees and disabled people worldwide. However, the rate of progress to develop neuroprosthetic limbs has been comparatively slow relative to other areas of robotics for two primary reasons: research involving neural implants with human subjects is very expensive and a lengthy process is required to obtain FDA approval to implant electrodes in human subjects. Thus, the overall goal of this project is to develop a virtual neuroprosthesis in which a facsimile of a neural implant is externalized and housed in a well-controlled microfluidic chamber, thereby abating the intrinsic limitations of highly invasive studies with neural implants. Upper limb amputee subjects will be recruited to control a dexterous artificial hand and arm with electromyogram signals while electroencephalogram (EEG) signals are simultaneously measured. Robotic grip force measurements will be biomimetically converted into electrical pulses similar to those found in the peripheral nervous system to catalyze in. vitro nerve regeneration after neurotrauma. The synergistic contributions of this multidisciplinary project will lead to a transformative understanding of the symbiotic interaction of neural plasticity within human-robotic systems. Currently, there is no systematic understanding of how tactile feedback signals can contribute to the neural regeneration of afferent neural pathways to restore somatosensation and improve motor function in amputees fitted with neuroprosthetic limbs. Tackling this problem will be a significant breakthrough for the important field of neuroprosthetics. The proposed virtual neuroprosthesis will be much less expensive and vastly simpler to obtain IRB approval to conduct research with human subjects. Through this, the research team can conduct meaningful neuroprosthetic experiments with human subjects at a fraction of the cost while accumulating significant data much quicker.